The Genesis Mission: Transforming Science and Energy with AI

DE-FOA-0003612

Office of Science (SC), Office of Critical Minerals and
Energy Innovation (CMEI), Office of Environmental
Management (EM), Office of Electricity (OE),
Hydrocarbons and Geothermal Energy Office (HGEO), and
Office of Nuclear Energy (NE)
The Genesis Mission: Transforming Science and Energy
with AI
Notice of Request for Application (RFA) Number:
DE-FOA-0003612
RFA Type: Initial
Assistance Listings: 81.049
RFA Issue Date: March 17, 2026
Submission Deadline for FY26 Phase I April 28, 2026, at 11:59 PM Eastern
Applications:
Submission Deadline for FY26 Phase II April 28, 2026, at 5 PM Eastern
Letters of Intent:
Submission Deadline for FY26 Phase II May 19, 2026, at 11:59 PM Eastern
Applications:
Submission Deadline for Phase II December 17, 2026, at 11:59 PM Eastern
Applications resulting from FY26 Phase I
Awards:

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Table of Contents
TABLE OF CONTENTS .................................................................................................................1
I. BASIC INFORMATION ............................................................................................................1
EXECUTIVE SUMMARY .........................................................................................................1
FUNDING DETAILS ..................................................................................................................2
KEY FACTS ................................................................................................................................2
KEY DATES ...............................................................................................................................3
SUBMISSION TEAMING REQUIREMENTS ..........................................................................3
AGENCY CONTACT INFORMATION ....................................................................................3
INFORMATIONAL WEBINAR / OFFICE HOURS .................................................................3
RECOMMENDATION ...............................................................................................................3
II. ELIGIBILITY ............................................................................................................................4
A. ELIGIBLE APPLICANTS ....................................................................................................4
B. COST SHARING ...................................................................................................................5
C. ELIGIBLE INDIVIDUALS ...................................................................................................6
D. LIMITATIONS ON SUBMISSIONS ....................................................................................6
E. OTHER ELIGIBILITY REQUIREMENTS ..........................................................................7
III. PROGRAM DESCRIPTION ....................................................................................................8
A. PURPOSE ..............................................................................................................................8
B. PROGRAM GOALS, OBJECTIVES, AND PRIORITIES .................................................57
C. AWARD CONTRIBUTION TO GOALS AND OBJECTIVES .........................................58
D. PERFORMANCE GOALS ..................................................................................................58
E. PROGRAM UNALLOWABLE COSTS .............................................................................59
F. CITATIONS TO STATUTE AND REGULATIONS .........................................................59
G. PROGRAM HISTORY .......................................................................................................59
H. OTHER INFORMATION ...................................................................................................60
IV. APPLICATION CONTENTS AND FORMAT .....................................................................61
A. PRELIMINARY SUBMISSIONS .......................................................................................61
B. APPLICATION ....................................................................................................................61
C. COMPONENT PIECES OF THE APPLICATION ............................................................61
D. INFORMATION THAT MUST BE SUBMITTED AFTER APPLICATION BUT
BEFORE AWARD ....................................................................................................................76
V. SUBMISSION REQUIREMENTS AND DEADLINES ........................................................77
A. ADDRESS TO REQUEST APPLICATION PACKAGE ...................................................77
B. UNIQUE ENTITY IDENTIFIER (UEI) AND SYSTEM FOR AWARD MANAGEMENT
(SAM.GOV) ..............................................................................................................................77
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C. SUBMISSION INSTRUCTIONS ........................................................................................78
D. SUBMISSION DATES AND TIMES .................................................................................78
VI. APPLICATION REVIEW INFORMATION.........................................................................80
A. RESPONSIVENESS REVIEW ...........................................................................................80
B. REVIEW CRITERIA ...........................................................................................................80
C. REVIEW AND SELECTION PROCESS ...........................................................................82
VII. AWARD NOTICES ..............................................................................................................85
A. TYPE OF AWARD INSTRUMENT ...................................................................................85
B. ANTICIPATED TIMELINE FOR NOTICE OF SELECTION FOR AWARD
NEGOTIATION ........................................................................................................................85
VIII. POST-AWARD REQUIREMENTS AND ADMINISTRATION ......................................86
A. ADMINISTRATIVE AND NATIONAL POLICY REQUIREMENTS .............................86
B. REPORTING .......................................................................................................................87
C. REPORTING OF MATTERS RELATED TO RECIPIENT INTEGRITY AND
PERFORMANCE (DECEMBER 2015) ...................................................................................87
D. INTERIM CONFLICT OF INTEREST POLICY FOR FINANCIAL ASSISTANCE ......87
IX. OTHER INFORMATION ......................................................................................................89
A. CHECKLIST FOR AVOIDING COMMON ERRORS ......................................................89
B. HOW-TO GUIDES ..............................................................................................................90
C. ADMINISTRATIVE AND NATIONAL POLICY REQUIREMENTS ...........................114
D. REFERENCE MATERIAL ...............................................................................................136
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I. Basic Information
U.S. Department of Energy (DOE)
Office of Science (SC), Office of Critical Minerals and Energy Innovation (CMEI), Office of
Environmental Management (EM), Office of Electricity (OE), Office of Nuclear Energy (NE),
and Hydrocarbons and Geothermal Energy Office (HGEO)
Executive Summary
The DOE Office of Science (SC), Office of Critical Minerals and Energy Innovation
(CMEI), Office of Environmental Management (EM), Office of Nuclear Energy (NE), Office of
Electricity (OE), and Hydrocarbons and Geothermal Office (HGEO) hereby announce interest in
receiving applications from interdisciplinary teams addressing the Genesis Mission National
Science and Technology Challenges to accelerate scientific discovery and research and
development (R&D) workflows using novel artificial intelligence (AI) models and frameworks.
By achieving AI advantage, these teams will advance the DOE's mission and ensure America’s
security and prosperity by addressing energy, environmental, and nuclear challenges through
science and technology. Teams are encouraged to leverage the extensive scientific and data
resources of the DOE/National Nuclear Security Administration (NNSA), the National
Laboratories, U.S. industry, and academia. The resulting AI models and workflows, if
successful, may be integrated into the American Science Cloud.
DOE is soliciting new FY26 Phase I small team and Phase II large team applications in
the following topic areas: advanced manufacturing, biotechnology, critical materials, nuclear
fission, nuclear fusion, quantum information science, semiconductors and microelectronics,
discovery science, and energy (see specific focus areas in Section III Program Descriptions).
In addition, this RFA will remain available to allow the recipients of FY26 Phase I
awards to apply for larger team Phase II awards. In a few weeks, DOE plans to amend the RFA
to clarify the LOI and application guidelines for FY26 Phase II awards. In FY27, DOE plans to
amend the RFA or to issue an alternative funding opportunity to update the topic and focus areas
to allow a second competition of Phase I small team applications and Phase II large team
applications.
Additional applications for Phase I and Phase II may be submitted after the
corresponding deadline listed on the cover of this RFA, however, DOE reserves the right to
decline such applications without review.
1. Genesis Mission Consortium Members
Members of the Genesis Mission Consortium1 are eligible to submit applications.
Funding will be provided using DOE’s Other Transaction Authority2 (OTA). For awards under
1 More information on the Genesis Mission Consortium is available at
https://www.genesismissionconsortium.org/
2 42 U.S.C. § 7256(a), (g) (1993)
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DOE’s OTA, administrative provisions of this RFA may not be applicable to an applicant or
applicant’s subcontractor.
Please note that membership in the Genesis Mission Consortium is not required to apply
to this RFA or to participate in selected projects.
Notes for applicants of all types:
Receipt of an award under this RFA does not grant membership or require membership in
the Genesis Mission Consortium.
Applicants may propose non-domestic entities as subrecipients in their applications.
Applicants are advised that successful applications that propose non-domestic entities as
subrecipients include a detailed demonstration of how the proposed non-domestic subrecipients
possess skills, resources, and abilities that do not exist among potential domestic subrecipients.
Funding Details
Expected total available funding DOE anticipates a total of $293.76 million in
prior and current fiscal year funds will be
used to support awards under this RFA.
Expected number of awards The exact number of Phase I awards will
depend on the number of meritorious
applications and the availability of
appropriated funds.
Expected dollar amount of individual awards Phase I: $500,000 to $750,000
Phase II: Envisioned as 3 to 5 times the
Phase I award.
Expected award project period Phase I: 9 months; Phase II: 3 years.
Key Facts
RFA Title The Genesis Mission: Transforming Science and Energy with AI
RFA Number DE-FOA-0003612
Announcement Type Initial
Assistance Listing 81.049
Statutory Authority The programmatic authorizing statutes are:
U.S. Department of Energy Organization Act (codified as amended at
42 U.S.C. § 7256) (2020).
Act of Jul. 4, 2025, Pub. L. No. 119-21, § 50404, 139 Stat. 500
(2025).
Energy Policy Act of 2005, Pub. L. No. 109-58, § 901, 965, 119 Stat.
594 (2005).
Energy Independence and Security Act of 2007, Pub. L. No. 110-
140, § 618, 1304A, codified at 42 U.S.C. § 17197, 17384a
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National Quantum Initiative Act, Pub. L. No. 115-368, § 401, 132
Stat. 5100, 5105 (2018).
The CHIPS and Science Act of 2022, Pub. L. No. 117-167, § 10731,
136 Stat. 1366 (2022).
Governing Uniform Administrative Requirements, Cost Principles, and Audit
Regulations Requirements for Federal Awards, codified at 2 C.F.R. Part 200
(2024).
U.S. Department of Energy Financial Assistance Rules, codified at 2
C.F.R. § 910 (2022).
U.S. Department of Energy, Office of Science Financial Assistance
Program Rule, codified at 10 C.F.R. § 605 (2014).
U.S. Department of Energy Other Transaction Agreements Rule,
codified at 2 C.F.R. § 930 (2025).
Key Dates
Key dates are printed on the cover of this RFA.
Submission Teaming Requirements
As described in this RFA, multi-institutional teams are required for responsive
submissions to this RFA.
Agency Contact Information
Grants.gov 800-518-4726 (toll-free)
Customer Support support@Grants.gov
Program Contact GenesisMissionNOFO@science.doe.gov for all inquiries.
Informational Webinar / Office Hours
DOE plans to hold an informational webinar about this RFA on Thursday, March 26,
2026 at 3 PM Eastern. Registration instructions and other details will be posted at
https://science-doe.zoomgov.com/webinar/register/WN_cByyhWASR72Do7yIDpe3_g.
Recommendation
DOE encourages you to register in all systems as soon as possible. You are also
encouraged to submit applications well before the deadline.
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II. Eligibility
A. Eligible Applicants
All types of domestic applicants are eligible to apply, except nonprofit organizations
described in section 501(c)(4) of the Internal Revenue Code of 1986 that engaged in lobbying
activities after December 31, 1995.
Federally affiliated3 entities must adhere to the eligibility standards below:
1. DOE/NNSA National Laboratories
DOE/NNSA National Laboratories are eligible to submit applications (either as a lead
organization or as a team member in a multi-institutional team) under this RFA but may not be
proposed as subrecipients under another organization’s application. If recommended for funding
as a lead applicant or as a team member, funding will be provided directly to the DOE/NNSA
laboratory, for example, through the DOE Field-Work Proposal System with the work being
conducted under the laboratory’s contract with DOE. No administrative provisions of this RFA
will apply to the laboratory or any laboratory subcontractor. Additional instructions for securing
authorization from the cognizant Contracting Officer are found in Section IX of this RFA.
2. Non-DOE/NNSA Federally Funded Research and Development Centers (FFRDCs)
Non-DOE/NNSA FFRDCs are eligible to submit collaborative applications under this
RFA but are not eligible to be proposed as subrecipients under another organization’s
application. Instead, they must submit their own application as a team member in a multi-
institutional team. If recommended for funding in a multi-institutional team, funding may be
provided through an interagency agreement to the FFRDC’s sponsoring Federal Agency.
Additional instructions for securing authorization from the cognizant Contracting Officer are
found in Section IX of this RFA.
3. Other Federal Agencies
Other Federal Agencies are eligible to submit collaborative applications under this RFA
but are not eligible to be proposed as subrecipients under another organization’s application.
Instead, they must submit their own application as a team member in a multi-institutional team.
If recommended for funding in a multi-institutional team, funding will be provided through an
interagency agreement. Additional instructions for providing statutory authorization are found in
Section IX of this RFA.
3 Institutions that are not DOE/NNSA National Laboratories, a non-DOE/NNSA FFRDC, or
another Federal agency are not Federally affiliated, even if they receive Federal funds or perform
work under a Federal award or contract.
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B. Cost Sharing
Applicants are expected to follow through on estimated cost share commitments
proposed in their applications if selected for award negotiations.
Unless otherwise specified for the topic, cost sharing is not required for basic and applied
research awarded under this RFA, except for-profit entities. For-profit entities, whether prime
recipients (lead organizations) or subrecipients (team members), are required to provide not less
than 20% cost share for both basic and applied R&D activities and 50% of the total project costs
for demonstration and commercial application tasks.4
This cost share is to be based on the portion of the total budget proposed by each for-
profit entity and is required to be not less than 20% of the total allowable R&D costs of that
entity. Applicants must include any required cost share in their proposed budget justification, if
applicable, as described in Section IV.D. All cost share funding must originate from non-federal
sources, unless otherwise permitted by law. Cost sharing amounts proposed in the budget
justification are subject to validation during the period of performance and/or during closeout of
the award. Some focus areas in this RFA have further instructions on cost share for applications
submitted under that topic.
Additionally, demonstrations of institutional or third-party commitment to the proposed
activity, as described in Section IV as an appending to the Project Narrative are strongly
encouraged for all applications.
Examples of non-Federal contributions that may be considered as demonstrating
institutional or third-party commitment include, but are not limited to, the following:
• The provision of space, facilities, equipment, or resources at no or reduced charge;
• The provision of release time for faculty;
• The provision of scholarship support for students;
• The waiver of facilities and administrative costs, in whole or in part; or
• Third party contributions (e.g., state, private entities, etc.).
The institutional commitment is not to be documented on the application’s budget:
institutional commitments are neither a formal nor a voluntary committed cost sharing, but it
must be described in Appendix 7 of the Project Narrative.
Cost sharing and institutional commitments may not include the following:
• Revenues or royalties from the prospective operation of an activity beyond the time
considered in the award;
• Proceeds from the prospective sale of an asset of an activity; or
• Other Federal awards.
Additionally, cost sharing may be required under a class patent waiver, if applicable, as
discussed in Section IX.
4 Energy Policy Act of 2005, Pub. L. 109-58, sec. 988..
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Cost sharing is not required of DOE/NNSA National Laboratories, other Federal
agencies, another Federal agency’s FFRDC, or their subcontractors at any tier. DOE/NNSA
National Laboratories, other Federal agencies, and another Federal agency’s FFRDC may
impose cost-sharing requirements on their contractors subject to their policies and procedures.
C. Eligible Individuals
DOE does not require that individuals be U.S. citizens or permanent residents to be
proposed as a Principal Investigator (PI) or in any other role under an award, but all personnel
working or proposed to work under an award must have the legal right to perform such work in
the jurisdiction where the work will be performed. Individuals at any stage of their career may
be proposed as a PI if they have the skills, knowledge, and resources necessary to carry out the
proposed research.
D. Limitations on Submissions
LIMITATIONS ON INSTITUTIONS
Applicant institutions are limited to no more than one application as the lead institution
per focus area for Phase I and Phase II applications combined. Phase II applications must list a
primary focus area but will have the option to list secondary focus areas. The primary focus area
will be used for determining limitations on institutional submissions.
There is no limitation to the number of applications for which the institution is not the
lead in a multi-institution team using collaborative applications.
Should DOE receive submissions in excess of the applicable limits, DOE reserves the
right, in its sole discretion, to request additional or clarifying information to ascertain the
institution’s intended submissions. Otherwise, DOE will consider the latest received
submissions to be the institution’s intended submissions.
Applications in excess of the limited number of submissions may be declined without
review.
LIMITATIONS ON PI
The PI on an application may also be listed as a senior or key personnel on an unlimited
number of separate submissions but can be the lead PI on only one application. However, the PI
on an awarded Phase I award may submit a Phase II proposal as part of the FY27 go/no-go
decision process.
PIs must be in a permanent or indefinitely extensible position at the applicant institution,
whether tenured, tenure-track, or a staff appointment. Individuals in term-limited appointments,
whether as adjunct, visiting faculty, fellows, or similar appointments, are not eligible to be
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proposed as a PI. Individuals in part-time permanent positions are eligible to be proposed as a
PI.
Individuals in a joint appointment are eligible to be proposed as a PI if work will be
performed at the applicant institution and if the PI is a paid employee of the applicant institution.
Individuals paid by another institution may not be named as the PI but may be named in other
senior/key roles. A paid employee is one that is on the applicant institution’s payroll, receiving
wages and benefits in accordance with the applicant institution’s normal wage and benefit
practices, and whose position is not governed by any arrangement, agreement, or contract
between the applicant institution and another institution.
Individuals receiving more than half of their salary and benefits from a DOE/NNSA
National Laboratory may not be named as the PI in an application submitted by an applicant
other than a DOE/NNSA National Laboratory, regardless of any arrangement between the
employing Laboratory and the applicant institution.
E. Other Eligibility Requirements
In Phase I, applicants must propose small teams with partner institutions from at least two
of the following categories: (1) DOE/NNSA National Laboratory or a Scientific User Facility5,
(2) Industry, and (3) Institute of Higher Education (IHE)/Non-profit/Other. In Phase II,
applicants will be expected to propose large teams with at least one partner institution from
categories (1) and (2). Inclusion of lead or partner institutions from category (3) are strongly
encouraged but not required. To meet this requirement, partners must provide intellectual
contributions to the proposed project but do not need to be funded by DOE.
It is envisioned that DOE funding for all industrial partners combined could be up to 20%
of the total requested budget for specific project-relevant research efforts.
Additional Eligibility Requirements may be identified in the listing of topics in Section
III of this RFA.
5 Office of Science Scientific User Facilities (https://science.osti.gov/User-Facilities) not located
at a DOE/NNSA National Laboratory are included in this category.
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III. Program Description
A. Purpose
The DOE Office of Science (SC), Office of Critical Minerals and Energy Innovation
(CMEI), Office of Environmental Management (EM), Office of Nuclear Energy (NE), Office of
Electricity (OE), and Hydrocarbons and Geothermal Office (HGEO) hereby announce interest in
receiving applications from interdisciplinary teams addressing the Genesis Mission National
Science and Technology Challenges to accelerate scientific discovery and R&D workflows using
novel AI models and frameworks. By achieving AI advantage, these teams will advance the
DOE's mission and ensure America’s security and prosperity by addressing energy,
environmental, and nuclear challenges through science and technology. Teams are encouraged
to leverage the extensive scientific and data resources of the DOE, the National Laboratories,
U.S. industry, and academia. Any resulting AI models, workflows, and data, will be integrated
into the larger Genesis Mission capabilities.
SUPPLEMENTARY INFORMATION
The Genesis Mission, as described in Executive Order 14363, is a dedicated, coordinated
national effort that will unleash a new age of AI-accelerated innovation and discovery that can
solve the most challenging problems of this century. Proposing teams are encouraged to read the
full Executive Order and to bring their best ideas to advance the Genesis Mission.
The DOE's initial activities in support of Genesis Mission, fulfilling section 50404 of
Pub. L. No. 119-21 and other aspects of DOE mission, include two core components: (1) The
American Science Cloud (AmSC) is a collaborative cloud ecosystem for scientific research, data
sharing, and analysis; and (2) The Transformational AI Models Consortium (ModCon) is
curating data and building foundational, self-improving AI models for science. These efforts are
collaborating with teams funded by the NNSA to build the Genesis Mission’s American Science
and Security Platform. Additional information on relevant Genesis Mission activities is included
in DOE’s December 10, 2025 AI for Science announcement.
To increase and enhance DOE’s portfolio of activities in support of the Genesis Mission,
this announcement seeks applications from interdisciplinary teams to address one of the
challenges listed below. These challenges embody critical facets of the Genesis Mission
National Science and Technology Challenges (Topics 1-17) and cross-cutting needs of the
platform (Topics 18-21). By using AI models and novel frameworks to achieve an AI
advantage, these teams will accelerate scientific discovery and advance the DOE's mission.
Teams are encouraged to leverage the extensive scientific and data resources of the DOE, the
National Laboratories, U.S. industry, and academia. Successful AI models and workflows
developed under this effort may be integrated into AmSC, ModCon, and/or other key
components of the Genesis Mission platform.
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Phased Program Structure
Projects funded under this solicitation are expected to propose an approach or cluster of
related approaches that will be pursued in two phases:
Phase I: In the initial phase, teams will design and demonstrate a clear, tangible research
workflow that incorporates AI with concrete evaluation of the potential for AI advantage.
Success may include demonstrating increased predictive power or scientific insight from
appropriately-curated data, more tightly coupling data and experiments to validate hypotheses,
building new models and analyzing their impact on discovery speedup, identifying scaling
metrics that show how performance improves with more data or computing resources, improving
and speeding up experimental workflows (e.g., through automation or AI-informed parameters),
or other proposed metrics that the team would like to be considered. The goal is to provide
quantitative analysis of whether a proposed approach is on a trajectory toward a transformative
scientific capability, justifying further investment.
Phase II: During the second phase, meritorious Phase I and new Phase II teams will
pursue the promising directions identified during the first phase. DOE envisions a level of effort
(including team size and budget) at 3 to 5 times the initial phase. Receipt of a Phase I award will
not be a prerequisite for submitting a letter of interest and application for Phase II. If a team
believes they have already achieved the goals of Phase I awards, they may apply directly for a
Phase II award in FY26. However, it is anticipated that most FY26 awards will be Phase I. An
amended RFA will be issued to provide updated instructions about the Phase II LOI and
application.
Genesis Mission Consortium
The Genesis Mission Consortium, announced on February 6, 2026, is a public-private
partnership that will support the strategic direction of the Genesis Mission, working
collaboratively to rapidly advance progress in science, energy and other emerging technologies,
and national security.
Consortium members intend to contribute computing power, AI tokens, technical
expertise, and/or in-kind support to advance the Genesis Mission goals and build the community.
The consortium will connect leading industry and academic organizations with DOE and the
National Laboratories and their resources. It will identify high-value partnerships among
members that catalyze data flows and promote novel data applications.
Critically, the consortium will also identify strategic partnerships between members and
non-members for select opportunities, including this publicly available RFA. The consortium
has a partnership initiative to help members and non-members collaborate for applications
related to DOE Genesis Mission opportunities (without direction from DOE), including for this
RFA. Potential applicants to this RFA are encouraged, but are not required, to engage with the
consortium’s partnership service to assist with team formation prior to application submission
and/or during an award term. Please see the consortium’s website for additional information.
Membership in the consortium is not a pre-requisite for eligibility for funding under this RFA.
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Applicants interested in leveraging the partnership service can do so by contacting the
Genesis Mission Consortium. Please see the consortium’s website for additional information on
how to leverage the partnerships service (https://genesismissionconsortium.org/).
Note that applicants wishing to take advantage of the consortium’s partnership service are
encouraged to do so as soon as possible. Opportunities for the partnership service to effectively
assist with partnership formation will become more limited within one week of the submission
deadline.
Genesis Mission Integration
Each team will be part of the Genesis Mission and will contribute to the relevant
components of the Genesis Mission in a coordinated fashion. A core philosophy of the Genesis
Mission is that the whole is more than the sum of the parts, and specifically, that both specific
developments (software, data, models, etc.) and best practices can be shared across the initiative.
As described above, Genesis Mission funding to date has primarily focused on building a
platform that will serve as a common resource for this sharing in addition to providing
underlying resources for data storage and transfer, AI model training and inference, etc. All
teams are expected to take advantage of the platform when practical, including by hosting data,
models, and other artifacts on the platform along with metadata making those artifacts
discoverable by, and reusable by, other appropriately authorized users.
The platform’s capabilities are provided by DOE/NNSA National Laboratories and other
Genesis Mission Consortium members, and the latest information on the current and expected
capabilities of the platform will be available for all projects selected under this RFA. The
Genesis Mission Consortium’s partnership service can be used to help connect potential
applicants with information and experts on the platform’s capabilities.
Accordingly, each team is expected to participate in coordination activities across the
Genesis Mission for data, models, and infrastructure, and will be expected to provide information
on project requirements, needs, and best practices as requested by the Genesis Mission.
Topics and Focus Areas
Each applicant must address a topic and focus area given below. Phase I applications are
limited to a single focus area. Phase II applications must identify a primary focus area but can
also address secondary focus areas. Cost share requirements are specific to each focus areas.
Note on funding or co-funding opportunities from the SC-ASCR program: Core
components of ASCR’s mission relevant to this solicitation include advancing applied
mathematics and computer science, including AI and quantum information science; concurrently
advancing applied mathematics and computer science with disciplinary science in critical areas;
and developing future generations of computing hardware and software tools for science and
engineering in areas ranging from high-performance computing through edge computing and
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laboratory/experimental automation. AI innovation is a priority for this funding solicitation. For
consideration by ASCR, applications must clearly articulate how the proposed work will advance
one or more aspects of ASCR’s mission as described above. ASCR intends to work with all
offices partnering on this solicitation to identify promising co-funding opportunities in all focus
areas listed below. Accordingly, focus areas listed below that ASCR is leading in multi-program
topics are tagged with ASCR, but ASCR will consider co-funding relevant applications
submitted in all areas.
CHALLENGE AREAS FOR APPLICATION FORMATION
1 - Reenvisioning Advanced Manufacturing and Industrial Productivity
Participating Offices: Office of Science-Basic Energy Sciences (SC-BES); Office of
Critical Minerals and Energy Innovation (CMEI)-Industrial Technologies Office (CMEI-ITO);
CMEI-Advanced Materials and Manufacturing Technologies Office (CMEI-AMMTO); SC-
Fusion Energy Sciences (SC-FES); SC-Advanced Scientific Computing Research (SC-ASCR)
Challenge: Bridging the gap between scientific discovery and commercially viable
products and processes, known as the “valley of death”, remains the biggest challenge in
industrial manufacturing. The scaling, modification, or deployment of innovative materials and
processes depends on a vast and complex parameter space, requiring the uncovering and
integration of underlying physical and chemical principles that hold the key to the next U.S.
manufacturing revolution.
AI Solution: Recent advancements in agentic and generative AI present opportunities to
accelerate discovery and translational science. These new tools could navigate multi-scale and
high-dimensional dynamic systems to find hidden relationships and uncover new manufacturing
routes and products. AI can also enable end-to-end digital transformation of industrial
manufacturing through integration of real-time data from machines, products, processes, and
supply chains into manufacturing digital twins for continuous human-in-the-loop automated
decision support and deployment of emerging advanced manufacturing technologies —including
digital twins of the equipment to manufacture the products.
Justification: The DOE/NNSA National Laboratories co-locate world-leading expertise
in the discovery, optimization, and scale-up of novel chemistry and materials for energy
applications and in smart manufacturing, as well as operate cutting edge synthesis and
characterization facilities and high-performance computing resources that lead to the next
generation of advanced equipment. At the same time, DOE has a long history of partnering with
industry, which has helped cultivate data from industrial facilities. This creates an ideal
environment to advance AI-driven manufacturing for U.S. industries today and in the future.
National impact: Delivering innovations through an efficient, distributed, and adaptive
platform capable of real-time decision making and end-to-end intelligence will profoundly
transform how we create chemicals and materials foundational to industrial products and
advanced energy technologies while strengthening supply chain resilience and competitiveness
of American industry to create jobs and economic growth.
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Focus Areas for FY 2026:
A. Agentic AI-Driven Chemical Manufacturing (BES)
Develop agentic-AI capable of reasoning, integrated with digital twins, and interacting
with self-driving and self-adaptive chemical laboratories as a new scientific paradigm to
investigate, predict, discover, and deploy new chemical processes, catalysts, and reaction
modalities using domestic resources that will impact the production and distribution of
large-scale critical chemical building blocks.
B. AI-Driven Materials Processing (BES)
Develop AI models to integrate datasets from multi-modal synthesis, characterization,
and fabrication techniques with high-throughput simulations and predictive theory,
leveraging the data-rich parameter space, including diverse synthetic methodologies and
bottom-up to top-down approaches, to accelerate the transition from novel material
discovery to rapid scale-up, integration, and deployment.
C. AI-Enabled Manufacturing for Extreme Energy Systems (FES)
Develop AI-driven digital twins for first-of-a-kind energy technologies, especially fusion
energy systems, that connect physics, materials behavior, manufacturing processes, and
systems-operations data and enable the design and fabrication of components as part of
qualified and matured systems that outperform bespoke prototypes with limited
operational ranges.
D. Digitalization of Industrial Processes (ITO)
Expand use of physics-informed AI methods, digital twins, and advanced controls for
processes and equipment across a variety of industrial sectors to optimize existing
processes and technologies; design, develop, and evaluate innovative technology and
process solutions; and enable real-time process control and dispatch of energy resources
or feedstocks. This focus area solicits applications that are relevant to energy-intensive
industries, including chemicals, cement and concrete, iron and steel, food and beverage,
refining, and pulp and paper, as well as in technologies and processes that are common
across multiple industrial sectors, such as process heating, membrane separations, load
flexibility, onsite power generation, thermal energy storage, water supply, wastewater
treatment, and process integration technologies. The proposed approaches should
provide proof-of-concept and be generalizable to other industrial contexts with the ability
to test, validate, and scale.
Potential applicants are strongly encouraged to include an industrial partner on the project
team.
E. AI-Enabled Smart Manufacturing (AMMTO)
Leverage AI-driven smart and secure manufacturing technologies across industrial
facilities and enable manufacturing digital transformations. Develop Agentic-AI for
manufacturing system level optimization and reasoning to simulate, train digital twins for
modeling real-time hyper-realistic smart factory floor that includes the exact layout,
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equipment states, robot positions, advanced automation and control, and even virtual
models of human workers.
F. Energy Material Manufacturing (AFFO)
Leveraging AI-enabled digital twins to optimize advanced synthesis and roll-to-roll
processes ubiquitous in the production of energy materials and solid electrolyte-based
electrochemical devices with complex properties and geometries, e.g., fuel cells. These
digital twins will integrate multi-physics models, real-time sensor fusion, and metrology
data to achieve comprehensive monitoring, predictive analysis, and closed-loop control in
complex, high-dimensional manufacturing environments. The goal is to accelerate the
end-to-end digital transformation of these processes, addressing challenges like data
latency and incompleteness, ultimately driving high-volume manufacturing,
commercialization, and significant cost reduction for advanced energy material and
device production. Work efforts should include hardware and software/algorithm
development and demonstration as necessary toward the technical objective of the
application. Applications are expected to be primarily DOE/NNSA National Laboratory
led and executed.
2 - Scaling the Biotechnology Revolution
Participating Offices: Office of Science-Biological and Environmental Research (SC-
BER); Office of Critical Minerals and Energy Innovation-Alternative Fuels and Feedstocks
Office (CMEI-AFFO); SC-Advanced Scientific Computing Research (SC-ASCR)
Challenge: Designing biology on demand to innovate in biotechnology and re-establish
U.S. leadership will require accurately defining the essential governing principles—from atoms
to molecules to organisms to ecosystems and back. However, the nonlinear complexity of
biological systems and long, costly development cycles hinder biotechnological innovations,
impeding the delivery of crucial biofuels, biochemicals, and bioproducts vital for American
economic prosperity and energy independence.
AI Solution: AI will integrate and interpret genomics, multi-omics, imaging, dynamics,
and phenomics data into embedded models to establish genotype–phenotype relationships,
identify causal control points, and guide autonomous experimentation. This will advance AI’s
ability to reason about long causal chains under uncertainty, integrate across multiple scales, and
learn from sparse, noisy data. Tools like digital twins could be used to derisk process
development, optimization, integration, and scale-up, thereby speeding up industrial production
of biofuels, biochemicals, and bioproducts by orders of magnitude.
Justification: DOE is uniquely positioned to leverage its world-class molecular and
genomic capabilities of the Lawrence Berkeley National Laboratory’s (LBNL) Joint Genome
Institute, the Pacific Northwest National Laboratory’s Environmental Molecular Sciences
Laboratory, the specialized feedstock and conversion capabilities of the Idaho National
Laboratory’s Biomass Feedstock National User Facility, the National Laboratory of the Rockies’
Integrated Biochemical Refinement Facility & Process Development Unit, and the LBNL
Advanced Biofuels/Bioproducts Process Development Unit facilities and high-performance
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computing. These DOE assets, combined with AI tools and digital twins, will enable predictive
design of biological processes, products, and systems and rapid scale-up of biomanufacturing.
National Impact: Accelerating AI-driven biotechnology will position the U.S. to
establish leadership in the biotechnology revolution by rapidly advancing the continuum from
discovery to biomanufacturing scale-up, in areas spanning energy, bio-based domestic sources of
critical minerals and materials, health, agriculture, and biosecurity, and will drive economic
growth and job creation across the Nation.
Focus Areas for FY 2026:
A. Biomolecular Science (BER)
Determine fundamental principles connecting protein structure with function.
The structural characterization of biomolecules, integrated with genomics and other
'omics technologies, is crucial for functional analysis of biological systems and the
acceleration of biosystems design applications underpinning biotechnology innovation.
Recent advancements in AI tools for protein structure prediction have been
transformative, offering new insights into biomolecular structure. Further development is
desirable using new integrative approaches spanning diverse instruments and platforms,
scalable automated workflows, and advanced AI models to explore the fundamental
linkages between biomolecular structure and function. Innovative AI capabilities are
requested for biomolecular design by integrating structural data from diverse platforms to
drive novel concepts in enzyme improvement and function. This includes enhancing
protein structure prediction tools, integrating genomics/omics with biomolecular structure
to elucidate function, and addressing gaps in metabolic pathway analyses for
microorganisms and plants. A key focus is on overcoming current limitations in
understanding the connection between biomolecular structure and function governing
molecular interactions, and molecular dynamics to advance foundational principles for
biosystems design.
B. Genotype to Phenotype (BER)
Link genomic, biomolecular, metabolic and environmental factors to predict
emergent phenotypic outcomes within microbes and plants in a systematic way that
incorporates non-linear, stochastic networks. Biological systems operate through
complex networks where genetics interact with various external influences. The
objective is to pioneer advanced artificial intelligence models designed to understand and
optimize microbes and feedstock crops, for enhanced domestic production of sustainable
fuels, chemicals, and materials. This involves strategically leveraging diverse multi-
modal data, encompassing multi-omics, and environmental datasets. By integrating these
data sources, AI models could elucidate the intricate mechanisms through which genetic
variations within the feedstock crops and microbial communities, alongside dynamic
environmental factors and interaction with the surrounding ecosystem, collectively
impact their emergent phenotypic outcomes. Additionally, a key target is to develop AI
innovations that significantly reduce data requirements, enabling more efficient model
training and reducing context-dependent predictions of phenotypic outcomes directly in
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field settings. This will deepen the understanding of complex biological systems, thereby
driving significant innovations in biotechnology.
C. Predictive Engineering of Microbial Communities (BER)
Develop a complete understanding of how genetic variation and environmental
dynamics determine the behavior and interactions within and among microbial
populations. Engineered microbiomes could be leveraged for advancing
biomanufacturing, resource extraction, enhanced plant health, and other DOE relevant
applications. AI will accelerate predictive microbiome engineering by enhancing our
understanding of how genetic variation and environmental dynamics determine the
behavior and interactions within and among microbial populations. Applications should
focus on AI tools to elucidate the function of genetic dark matter that dominates
metagenome data, how to predict novel metabolic pathways, and to identify/engineer
interspecies metabolic networks. Also of interest are comprehensive AI-driven models to
predict microbiome gene expression across the phylogenetic spectrum and under a broad
set of conditions to drive the engineering of community function for a range of
environmental, biotechnology and biomanufacturing applications.
D. Bio Design (BER)
Utilize advances in fundamental predictive principles to design, control, optimize
and incorporate multiple traits within plants and microorganisms. The standard strategy
for engineering biology follows the so-called Design-Build-Test-Learn (DBTL) iterative
cycle. The incorporation of innovative AI approaches can dramatically accelerate this
process not only by facilitating the discovery of new molecular mechanisms and
harnessing them for engineering purposes, but also by enabling accurate prediction of the
behavior of engineered organisms. Having enough functional, AI-ready data, along with
new predictive AI approaches to parse the data will ultimately eliminate the need for
testing and learning, effectively transforming the DBTL approach into a “design and
build” (DB) paradigm. To advance DBTL towards this DB paradigm change,
applications should generate/analyze high-throughput multi-omics and other AI-ready
data from DOE-relevant plant or microbial systems to train, develop and deploy
innovative AI approaches for bioengineering. These advances will enhance prediction of
engineering outcomes and accelerate discovery of broadly compatible, precisely tunable
genome engineering tools and practices.
E. AI-Enabled Biological Reaction Engineering, Bioreactor Design, Process Scale-up
and Integration (AFFO)
Develop AI/digital twin models to streamline R&D, predict and address the
challenges and complexities involved in the biological system, bioreactor design, and
process scale-up and integration to enable cost-effective production of biofuels,
biochemicals, and bioproducts. Technology areas for AI models include but are not
limited to: algal and/or bacterial strain development for enhanced traits, productivity,
and/or novel capabilities; building microbial communities to targeted specifications;
closed-loop autonomous biosystem design; precise prediction, control, and performance
optimization of bioreactors and cultivation systems that enhances mass/heat transfer,
mixing, cell viability, and product yield; addressing cost and reliability challenges in
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biological process scale-up and integration; upgrading of biochemically formed
molecules into high quality high value final products via thermo-catalytic process.
Applications should consider integrating experimental data and information at laboratory,
pilot, and/or industry relevant scales. Applications should create and assemble data
repository for AI model development, evaluate and establish necessary digital model
framework for baselining and validation with their own data. Applications should
develop metrics to demonstrate the proof-of-concept of the proposed AI-driven
approaches and quantify the acceleration of biotechnology and biomanufacturing. The
examples of metrics include but are not limited to: prediction accuracy, real-time
autonomous control decision, process stability and robustness, product yield, cell
viability and stability, batch-to-batch variability, process/reactor downtime, resource
efficiency, data quality and integrity, model adaptability to new condition/products,
computational cost to train and run the AI models, cost savings, accelerated design and
scale-up, time-to-market reduction, etc.
3 – Securing America’s Critical Minerals Supply
Participating Offices: Office of Critical Minerals and Energy Innovation (CMEI)-
Analysis and Strategy Office (ASO); CMEI-Advanced Materials and Manufacturing
Technologies Office (CMEI-AMMTO); CMEI-Advanced Mining and Minerals Production
Technologies Office (CMEI-AMMPTO); SC-Basic Energy Sciences (SC-BES); SC-Biological
and Environmental Research (SC-BER); SC-Advanced Scientific Computing Research (SC-
ASCR)
Challenge: America’s dependence on foreign supply chains for critical minerals and
materials (CMM) threatens national security, economic competitiveness, and the deployment of
technologies essential for energy independence. Domestic critical mineral production is
expensive, complex, and time-consuming, in part because of the many steps to identify, extract,
refine, and concentrate from complex, heterogeneous sources across critical mineral supply
chains.
AI Solution: AI will revolutionize the entire critical minerals supply chain and
development of alternative materials by integrating geophysical data, other fundamental science
data, process optimization, cost estimation, and economic modeling into one connected system.
Solving this challenge demands an AI that can reason scientifically, can understand complex
structure-property relationships, and can design alternatives with different compositions.
Physics-based AI offers advanced predictive capabilities to identify alternatives and understand
processes underlying critical mineral availability, recovery, refinement, and replacement.
Justification: DOE’s existing minerals characterization datasets (e.g., METALLIC,
CMI Hub), combined with DOE/NNSA National Laboratory expertise and DOE-supported
efforts in materials science, chemistry, geosciences, biology, process engineering, and economic
modeling, could enable acceleration from the years-long mineral development timelines to rapid
resource assessment and production optimization. Further, use of AI could reveal new strategies
to replace and/or eliminate the need for CMMs in some materials and chemical processes.
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National Impact: This effort will reduce reliance on adversarial nations, expand America’s
mineral resource base, maximize production profitability, and strengthen supply chain resilience
for technologies essential to national security and economic prosperity.
Focus Areas for FY 2026:
A. Resource Mapping and Development (AMMPTO)
Accelerate expansion of America's mineral resource base by combining cutting-
edge geophysical and chemical data with AI-powered process prediction and cost
estimation to derisk projects, leveraging existing minerals characterization datasets from
existing DOE/NNSA National Laboratory efforts (e.g., METALLIC, CMI Hub).
B. AI-Enabled Materials Discovery and Engineering (AMMTO)
Deploy automated discovery platforms to accelerate material discovery,
manufacturing, and qualification through closed-loop systems that integrate hypothesis,
experiment, validation, and manufacturing, reducing demand for critical minerals.
C. Economic Modeling and Market Analysis (ASO)
Implement ATHENA (AI-Toolkit for Holistic Economic Network Analysis) to
synthesize critical mineral supply and demand datasets, enabling scenario analysis of
policy interventions and geopolitical disruptions on critical mineral markets and critical
materials supply chains informed by relevant third-party datasets (Benchmark Minerals
Intelligence, Bloomberg BNEF, S&P Global, and potentially others).
D. Extraction and Processing Technologies (AMMPTO, AMMTO)
Develop AI-enhanced technologies for domestic extraction, mid-stream
processing, refining, and recycling of critical minerals to establish complete domestic
supply chain capabilities, leveraging existing minerals processing data from existing
DOE/NNSA National Laboratory efforts (e.g., METALLIC/PROMMIS).
E. Geological Finders/Keepers (BES, BER)
(i) Develop AI models that integrate geophysical, advanced sensing, and soil system
Insights of fluid dynamics and interfacial interactions with the geochemistry of
mineral/interface interactions to predict the location and extraction of CMMs from
the subsurface and from unconventional sources such as mine tailings. Expand lab-
scale model systems to incorporate real-world field-scale heterogeneous systems.
(ii) Develop AI capabilities that integrate geophysical, advanced remote sensing, and
subsurface system insights to locate, characterize, recover, and extract CMM from
surface soils/water, the subsurface/groundwater, plants, and waste streams. Through
the use of AI, utilize a wide range of systems approaches to study field sites,
including the integration of hydrologic and geochemical findings with microbial
genomics and rhizosphere processes into a range of reactive transport models to cover
scales from molecular to regional. Expand lab-scale model systems to incorporate
real-world, field-scale heterogeneous systems, incorporating mechanistic and
quantitative knowledge across the range of interfacial processes and advanced
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translation of AI-ready data into numerical models for projections of recovery
options.
F. Connections for Isolation (BES)
Utilize AI models to identify chemical drivers that significantly improve the
separation of CMMs from heterogeneous mixtures, incorporating knowledge of
properties at multiple length and time scales, quantum through macroscopic properties,
molecular interactions, and energy exchanges.
G. Biological Pathways to CMM (BER)
Employ AI-guided discovery of microbes, proteins, peptides, and pathways within
a biological system or as a component of a biohybrid approach to significantly improve
the efficiency of CMM recovery, separations, and refinement for industrial use. Use AI
to decode the molecular-level rules of how microbes interact with minerals to enhance
CMM extraction. The unique properties of biological molecules and organisms may
present novel opportunities to recover CMMs from dilute substrates. Applications should
focus on ways to leverage AI to understand the molecular-level rules of microbe-mineral-
plant interactions, including the analysis of experimental and computational data to
delineate the fundamental molecular mechanisms of mineral interaction, uptake,
translocation, and biological sequestration. Also of interest are AI tools to design and
engineer enhanced plant and microbial bio-recovery systems via engineering of novel
proteins, peptides, or metabolic pathways to improve CMM selectivity, recovery rates,
and tolerance to harsh industrial conditions. This might include ways to engineer CMM
affinity from molecules to organism in plants and across the microbial phylogenetic
spectrum.
4 - Delivering Nuclear Energy that is Faster, Safer, Cheaper
Participating Offices: Office of Nuclear Energy (NE); Office of Science-Advanced
Scientific Computing Research (SC-ASCR)
Challenge: Nuclear power plants and facilities have historically been challenged by long
development timelines and burgeoning costs, limiting America’s ability to deliver affordable,
resilient, and reliable energy as demand continues to grow—particularly from AI data centers.
AI Solution: This initiative will accelerate nuclear energy deployment by using AI to
design, license, manufacture, construct, and operate reactors with human-in-the-loop workflows,
enabling at least 2x schedule acceleration and greater than 50% operational cost reductions. To
meet these goals, DOE will develop through this RFA a suite of explainable AI solutions
including surrogate models, agentic workflows, autonomous labs, and digitals twins. For
example, for reactor operations, we will use digital twin systems with AI components that will
interpret complex operational data in real time.
Justification: DOE's combination of national laboratory nuclear expertise, test facilities
(e.g. Idaho National Laboratory's Advanced Test Reactor, Transient Reactor Test Facility, Fuel
Conditioning Facility, Hot Fuel Examination Facility, and Collaborative Computing Center; Oak
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Ridge National Laboratory's High Flux Isotope Reactor; Argonne National Laboratory’s
Mechanisms Engineering Test Loop Facility), decades of operational data, regulatory
partnerships, industry partners, and extensive computational ecosystem uniquely position it to
accelerate reactor deployment.
National Impact: This moonshot will provide Americans more affordable energy while
reducing human error, strengthening national security, and directly supporting U.S. energy
dominance with multi-billion-dollar cost savings per gigawatt of generating capacity.
Focus Areas for FY 2026:
The Office of Nuclear Energy seeks proof of concept demonstrations and gated, outcome-
driven applications that leverage AI technologies to measurably accelerate nuclear engineering
and licensing pipelines in support of urgent national energy security and clear firm power
deployment objectives. Applications should demonstrate quantifiable reductions in cost,
schedule, and regulatory uncertainty while maintaining rigorous standards for traceability,
verification and validation, configuration control, and human oversight appropriate for high
consequence systems. Collaborative, focused teams involving U.S. laboratories, reactor
developers, reactor operators, and relevant industry partners will leverage collective expertise,
ensure interoperability, and secure data governance, and define clear transition pathways from
demonstration to deployable capability that maximizes real world impacts.
A. Accelerated Nuclear Power Plant Design and Licensing: Create an automated
process to enable rapid design, including safe and secure autonomous monitoring
and control of plant operations, licensing considerations, and rapid deployment of
advanced nuclear technologies using AI.
INDUSTRY COST SHARE: Required (20% for R&D and 50% for Demonstration)
To accelerate the deployment of advanced nuclear technologies, applications are sought
to develop and demonstrate AI enabled tools that streamline nuclear reactor design and
licensing under human authority. This call invites concepts that leverage AI to automate
and enhance the preparation and review of safety and regulatory documentation,
harmonize regulatory requirements, and integrate reasoning engines with
multidisciplinary engineering workflows spanning requirements definition, analysis,
multiphysics modeling, and design synthesis. Proposed efforts should enable rapid
exploration of component level analysis, subassembly analysis, balance of plant to the
full operation of the reactor; and optimization of reactor configurations for diverse
applications and tighter integration between design and licensing processes to reduce,
cost, schedule and regulatory uncertainty.
B. Autonomous Power Plant Operations: Develop AI digital twin systems that
interpret plant operational data in real time, detect anomalies, and recommend
preemptive actions to maintain safety and operational performance.
INDUSTRY COST SHARE: Required (20% for R&D and 50% for Demonstration)
Applications are sought to develop AI digital twin systems for autonomous power plant
operations, with a focus on small modular reactors (SMRs) and microreactors. This
includes leveraging AI/ML to enhance existing sensor technologies and to develop novel
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instrumentation that fills identified measurement gaps, enabling more accurate, reliable,
and integrated data streams that strengthen digital twin fidelity and real‑time decision
support. Applications that leverage DOE/NNSA National Laboratory resources and
advanced statistical control algorithms to demonstrate safer, more efficient, and
responsive autonomous reactors and include a focus for developing provable cyber
assurance (PCA) for security and threats are encouraged.
C. AI-Assisted Manufacturing and Construction: Support site selection, born certified
manufacturing, construction, supply chain reliability, and factory modular
production methods with AI technologies.
INDUSTRY COST SHARE: Required (20% for R&D and 50% for Demonstration)
Applications that leverage AI, predictive analytics, and automated delivery of a nuclear
ready component or construction ready support, including advanced manufactured
components, and qualification processes, for capacity expansion and reactor
demonstration, i.e., certified and licensable, will be considered. Applications that will
accelerate the availability of U.S.-based SMR technologies into domestic and
international markets and fortify the SMR supply chain, including supplier development
and component fabrication for deployment are desired.
D. Autonomous Research and Development: Condense nuclear material research and
qualification timeframes using AI-driven pipelines for modeling, characterization,
evaluation, and qualification, while integrating decades of global historical
irradiation data.
INDUSTRY COST SHARE: Required (20% for R&D and 50% for Demonstration)
Reactor deployment and continued operation relies on the utilization of qualified
structural and fuel materials and AI tools that significantly reduce the time and cost to
qualify new materials and manufacturing methods for nuclear energy deployment,
leveraging autonomous R&D, AI-driven pipelines for discovery and characterization,
coordination with standard and regulatory organizations, and curated irradiation data are
sought. Specific topics that address AI-driven generation of irradiation and other
environmental effects (e.g., corrosion) testing protocols, autonomous AI platforms for hot
cell laboratories, sensor systems, and time-dependent property predictions from low
sample sizes and standardized microscopy analysis are encouraged.
E. Accelerated Fuel Cycle Facility Design and Licensing to Secure the Domestic Fuel
Supply: Create automated processes to enable rapid design, licensing
considerations, and accelerated deployment of advanced fuel cycle technologies
using AI.
INDUSTRY Cost Share: Required (20% for R&D and 50% for Demonstration)
To accelerate the deployment of advanced nuclear fuel cycle technologies, applications
are sought to develop and demonstrate AI enabled tools that streamline fuel cycle facility
(e.g., mining, conversion, enrichment, deconversion) deployment and licensing under
human authority. This call invites concepts that leverage AI to automate and enhance the
preparation and review of safety and regulatory documentation, harmonize regulatory
requirements, and integrate reasoning engines with multidisciplinary engineering
workflows spanning requirements definition, analysis, modeling, and design synthesis.
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To improve the availability and utilization of high assay low enriched uranium
(HALEU), proposals are sought that develop and implement AI capabilities, including
machine learning and large language models, to enhance HALEU production processes,
distribution logistics, data integration, and lifecycle decision-making
To accelerate the domestic supply of uranium, including domestic uranium
exploration and extraction, proposals are sought to implement machine learning and
advanced data analytics for methodologies to automate and improve the accuracy of
geological data analysis, encompassing the integration of diverse well field geospatial,
geophysical, and geochemical data, training and development of predictive models,
enhanced spectral interpretation, operational optimization, and real-time decision-
making.
To accelerate the deployment of advanced nuclear fuels to provide abundant
energy, proposals are sought that implement AI tools to execute nuclear fuel irradiation
experiments that, in real time, integrate instrumented tests with digital twins to
demonstrate methodologies that accelerate fuel development and qualification.
To ensure effective usage of materials by closing the nuclear fuel cycle, proposals
are sought for AI/ML models which can optimize coupled reprocessing flowsheets and
waste disposal systems in terms of plant economics, waste costs, and waste minimization.
In addition, proposals are sought which use artificial intelligence to identify materials,
minerals, or glasses which have promise as advanced waste forms for chloride or
fluoride-bearing wastes.
F. AI-Assisted Site Characterization: Accelerate waste disposition site characterization
through AI Modeling.
INDUSTRY COST SHARE: Requires (20% for R&D and 50% for Demonstration)
Proposals that leverage the collation, evaluation, and analysis of large data sets from
multiple data sources, including but limited to USGS, state geologic surveys, and the oil
and gas industry to create detailed cross-section extrapolations and 3D models to enable
DOE to conduct a subsurface fatal flaw analysis of the feasibility of a potential site for
waste disposition of used nuclear fuel and/or reprocessed fuel waste streams.
G. AI-Assisted End Disposition Design: Concept Design for Disposal of Used Nuclear
Fuel and Reprocessed Fuel Waste Streams.
INDUSTRY COST SHARE: Requires (20% for R&D and 50% for Demonstration)
Given a combination of reprocessed fuel, high-level waste, and used nuclear fuel, DOE is
interested in proposals that use AI to design a licensable mined geologic repository in one
of the three main geological media, specifically shale, salt and granite. Proposals should
take into account the current and future used nuclear fuel inventory and incorporate the
use of deep boreholes as a disposition pathway for high-level waste.
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H. Development, Utilization and/or Adoption of AI and ML Tools to Support the
Efficient Review, Classification and Release of Legacy Documents to the Nuclear
Industry.
INDUSTRY COST SHARE: Requires (20% for R&D and 50% for Demonstration)
The information contained in legacy documents has the potential to shorten technology
development life cycles and reduce costs by allowing the nuclear industry to leverage
existing work. However, the existing DOE processes for enabling access to legacy
documents are challenged by the time, effort and cost associated with identifying,
reviewing and appropriately marking the documents (e.g., export-controlled information
(ECI)). This focus area encourages the development, utilization and/or adoption of AI
and ML tools that would enable DOE national laboratories, plants and sites to
significantly reduce the time and costs associated with reviewing and appropriately
marking legacy documents for efficient release to the nuclear industry.
Cost Share and additional Program Requirements:
• R&D: Projects focusing on research and development related to advanced nuclear
reactor technologies or AI applications for nuclear systems require a 20% industry
cost share.
• Demonstration: Projects involving the demonstration of nuclear reactor prototypes,
AI-driven operational enhancements, or other relevant technologies require a 50%
industry cost share.
• Nuclear Energy Sector Team Member Requirement: Applications must include a
nuclear sector team member (either as a prime or sub-recipient) capable of providing
real operational data essential for the development, demonstration, and validation of
the proposed technology. This team member must be located in the United States and
could include, but is not limited to:
Nuclear Reactor Vendors and Suppliers: Companies involved in the
o
design, construction, and operation of nuclear reactors.
Engineering, Procurement, and Construction (EPC) contractor:
o
Responsible for building and delivering a full nuclear power plant.
Operators of Nuclear Power Plants or other Fuel Cycle Facilities: Entities
o
managing existing nuclear facilities.
Federal Funding Research and Development Centers (FFRDCs):
o
Especially those with a focus on nuclear energy research and development,
collaborating with nuclear reactor vendors.
Utilities with Nuclear Assets: Investor-owned electric utilities or public
o
power entities that operate nuclear power plants.
AI Companies specializing in Nuclear Applications: Firms developing AI
o
solutions for nuclear safety, efficiency, or advanced reactor control systems,
partnered with a nuclear energy provider.
Regional Transmission Organizations/Independent System Operators
o
(RTOs/ISOs): Where the proposed technology directly impacts grid
integration of nuclear energy.
Electric wire owning and/or operating entities: If the demonstration
o
involves grid-scale integration of nuclear energy solutions.
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5 - Accelerating Delivery of Fusion Energy
Participating Offices: Office of Science-Fusion Energy Sciences (SC-FES); SC-
Advanced Scientific Computing Research (SC-ASCR); Office of Nuclear Energy (NE)
Challenge: Realizing fusion energy on the grid requires coordinated progress across six
tightly coupled challenge areas defined in the Fusion Science and Technology Roadmap.6
Isolated, device-specific trial-and-error approaches cannot manage these interdependencies at the
scale, complexity, or pace required to meet national energy objectives.
AI Solution: AI enables physics-constrained digital twins that integrate plasma, nuclear,
materials, and system behavior within a unified predictive framework, allowing performance and
engineering trade-offs, failure modes, and design margins to be evaluated consistently in
simulation and experiment. An AI-Fusion Digital Convergence Platform (DCP) will integrate
novel algorithms in HPC codes, foundation models for plasma and materials science, physics-
and chemistry-informed neural networks, surrogate models, and digital twins for whole-facility
modeling and real-time control across the six Roadmap challenge areas. The DCP will
accelerate infrastructure development, shorten innovation cycles, and support a competitive U.S.
fusion ecosystem. The DCP also advances foundational plasma science and technology.
Justification: DOE uniquely brings together fusion facilities, national laboratories,
leadership-class computing, data stewardship, and public-private partnerships to build and
operate a trusted, national-scale AI platform that integrates data, models, and experiments across
the fusion ecosystem. That platform will leverage large-scale domestic and international fusion
facilities and fusion materials and technology infrastructure including high-heat-flux testbeds,
tritium and blanket test stands and loops, irradiation facilities, and in situ and in operando
materials characterization capabilities across both public and private sectors to meet Fusion
Roadmap milestones.
National Impact: The DCP would accelerate delivery of fusion energy as a source of
firm, scalable baseload power, strengthening U.S. energy security and competitiveness.
Focus Areas for FY 2026:
A. Structural Materials (FES)
Centralize material characterization datasets and train AI models to support the
engineering, design, development, and qualification of materials that can withstand the
high neutron flux, thermal loads, and environmental stresses of a fusion power plant.
(i) Multiscale, Multiphysics Modeling: Developing high-quality databases and models
that can connect femtosecond, atomistic interpretations to lifetime-scale, bulk
descriptions of materials.
(ii) Materials Qualification, Manufacturing, and Design: Development of high-quality
databases and models that can discover novel materials and/or material architectures for
6 The Fusion Science and Technology Roadmap is available at https://www.energy.gov/fusion-
energy.
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the structural components of fusion pilot plants. Develop tools which can aid in the
definition of relevant code cases to certify/license fusion reactor materials
B. Plasma-Facing Materials (FES)
The Plasma-Facing Materials area is interested in applications addressing the same
challenges described in the Structural Materials focus area. Additionally, deploy digital
twins of heat and plasma exposure facilities to support the characterization of physical and
mechanical properties, manufacturing, and qualification of materials that directly interact
with the plasma and face the most extreme temperatures, neutron fluxes, and stresses.
(i) Multiscale Modeling and Materials Qualification, Manufacturing, and Design: As
described in the above section, but instead for plasma-facing materials.
(ii) Divertor design and optimization: Development of novel divertor concepts that
maximize performance in fusion-relevant conditions, including burning plasmas.
(iii) Pedestal Prediction: Development of capabilities that predict the performance of
the edge plasma region, built from a wealth of experimental data, simulations, and first-
principles modeling.
(iv) Core-edge Integration (including wall components): Development of models that
connect core heating performance with operation in the plasma edge that mitigates
thermal loads, or employs novel surfaces (e.g., liquid metals), on plasma-facing
components.
C. Advancing Confinement Approaches (FES)
Apply AI and machine learning within real-time plasma control systems and between
successive experimental pulses to create, sustain, and optimize fusion-relevant plasma
scenarios, including using autonomous methods, with the goal of achieving fusion-relevant
confinement regimes and sustained energy output. For IFE, develop physics-informed AI
surrogates and HPC workflows for targets and drivers, and apply real-time operational
feedback loops and inter-shot analysis frameworks.
(i) Plant/plasma State Optimization (Digital Twin): Development tools for control and
prediction of turbulence and stability that operate concurrently with plasma discharges,
forming bi-directional data flows. Development tools for driver-target coupling,
plasma instabilities reduction, and fusion gain optimization.
(ii) High-Fidelity Plant Design: Development of models for the design, construction, and
logistics of a Fusion Pilot Plant Facility.
(iii) Scenario Planning and Operations Co-pilot: Development of predictive tools that
optimize plasma performance and facility configuration to support efficient facility
operation.
D. Fuel Cycle and Tritium Processing (FES, NE)
Curate test loop data and develop AI models that track, forecast, and optimize tritium
separation, storage, inventory, and accountancy across components and systems to enable
fuel self-sufficiency and mature supporting technologies such as permeation barriers and
detritiation systems. Tritium storage, processing, and permeation: Development of models
of the nuclear effects associated with tritium handling. Advanced Component
Manufacturing: Development of models for the design and engineering of tritium-related
components.
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(i) Regulatory Analysis: Development of models that navigate the regulation requirements
for the handling of tritium.
E. Tritium Breeding Blankets (FES, NE)
Build a standardized database for thermophysical corrosion, tritium-related effects, and
mechanical properties; and deploy multi-physics AI models to advance blanket concepts
(e.g., solid, liquid, molten salt).
(i) Plasma-blanket interactions: Development of models that predict the effects of plasma
transients on breeding blankets.
(ii) Tritium Breeding Optimization: Development of models that maximize the tritium
breeding ratio for a Fusion Pilot Plant concept.
(iii) Neutronics: Development of models for neutron interactions with blankets and
materials.
F. Fusion Plant Engineering and System Integration (FES)
Launch a centralized, standards-based data repository for experimental data and whole
plant simulations, connecting AI, HPC, and digital twin capabilities to key fusion facilities
in public and private sectors to advance balance-of-plant technologies such as power
conversion and plant-wide control systems, as well as remote maintenance and robotics.
(i) Remote Maintenance: Development of digital tools that provide warnings for end-of-
cycle components, flagging potential issues before breakdown.
(ii) Robotics: Development of automatons for maintenance of systems and assembly that
may otherwise be impossible for human technicians.
(iii) Facility Construction: Development of models that plan the entire lifecycle of
Fusion Pilot Plant construction before breaking ground.
G. Plasma Science and Technology (FES)
This area is interested in AI and machine learning techniques to enhance control, analyze
large datasets, automate detection and prediction, and accelerate experimental throughput
and discovery in plasma science and technology.
(i) Apply AI and ML methods to magnetic reconnection (multiple X-line kinetic
reconnection) process which occurs in most of the space (solar and magnetospheric)
and laboratory magnetic fusion plasmas. Automate detection, classification, and
analysis of complex, transient plasma regions and structures in three dimensions such
as plasmoids within large-scale simulation, laboratory, and observational data to
identify magnetic reconnection events in real-time, improve efficiency, the
understanding of the reconnection rate and how magnetic energy converts to particle
energy, and the ability to predict future events.
(ii) Apply AI and ML methods to create autonomous, optimized industrial plasma-based
systems that revolutionize applications across advanced manufacturing, healthcare, and
agriculture. Efforts will develop a predictive, mechanistic understanding of complex
physical processes such as plasma-mediated chemistry, plasma-surface interactions,
plasma-assisted processes, and plasma-soft matter interactions. These AI-driven
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insights will be used to design and control novel plasma synthesis techniques with high
precision and repeatability. The overall end goal is to drastically increase the industrial
impact of these technologies, lower their complexity, and break down existing barriers
to broader adoption.
(iii) Apply AI and ML at LaserNetUS facilities by enabling real-time optimization and
autonomous control of complex laser systems to accelerate experimental throughput
and discovery in areas of high-energy-density physics.
6 - Transforming Nuclear Restoration and Revitalization
Participating Offices: Office of Environmental Management (EM); Office of Legacy
Management (LM); SC-Advanced Scientific Computing Research (SC-ASCR)
Challenge: DOE's environmental cleanup mission faces an estimated $540 billion
liability over eight decades with ~90 million gallons of highly radioactive tank waste requiring
treatment and numerous assets requiring disposition that impedes site remediation and
restoration crucial for revitalization of American energy, security, and innovation.
AI Solution: A multimodal AI foundation model will be trained on DOE EM's
unparalleled 30+ years of operational data from unique nuclear processing facilities to predict
scale-dependent behavior across lab, pilot, and full-scale systems. DOE/NNSA National
laboratory experts will leverage Genesis Mission supercomputing capabilities and partner with
industry experts for accelerated simulation architecture in development of the AI models. The
goal is to use AI to enable mission acceleration to meet EM’s 2040 vision with significant
liability reduction.
Justification: DOE EM's unique data assets from designing and operating large-scale
facilities at complex sites (e.g., the Defense Waste Processing Facility and the Salt Waste
Processing Facility at the Savannah River Site (SRS)), combined with the capabilities provided
by the industry partners, will enable development of scale-bridging AI models that safely and
efficiently address deployment challenges (e.g., the Waste Treatment and Immobilization Plant
at the Hanford Site) no other institutions can leverage.
National Impact: This transformation will compress deployment timelines and
accelerate nuclear remediation, thereby enabling renewed use of nuclear materials (for energy
and medical applications) and infrastructure for American energy dominance.
Focus Areas for FY 2026:
This topic area represents a collaboration between EM, LM, and ASCR, and accordingly,
all applications in response to the following focus areas should be responsive to the ASCR co-
funding requirements noted in the Program Description section above the Challenges section.
A. EM AI R&D Roadmap Implementation (EM-3.2, ASCR, LM)
INDUSTRY COST SHARE: Required (Up to 50% cost share with industry partners,
including in kind, depending on specific scope of work the partner will perform)
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Coordinate AI investments across EM sites by identifying and prioritizing needs,
converting historical data into standardized formats, and evaluating multi-modal data
assets for AI applications. The ability to clean, standardize, and prepare vast multi-modal
data repositories – along with the intelligent retrieval, synthesis, and transfer of that
information into functional knowledge and mathematical representations – is central to
accelerating environmental management decision-making. Examples include
environmental characterization, detailed process sampling, remediation, monitoring, and
site/facility operations datasets. The data must be well documented allowing for
harmonization across formats and time horizons to support advanced analytic
applications and informed decision making across the EM’s complex operations, LM’s
activities, and interim needs at other DOE sites with similar challenges. In parallel,
develop integrated knowledge management systems for training of the next-generation
workforce.
B. Scale-Bridging AI Foundation Model (EM-3.2, ASCR)
INDUSTRY COST SHARE: Required (Up to 50% cost share with industry partners,
including in kind, depending on specific scope of work the partner will perform)
Develop a multimodal AI model using EM operational data, accelerated simulation
toolkits, and Genesis Mission supercomputing resources to predict scale-dependent
behavior. This focus area will develop a multimodal AI foundation model designed to
transform nuclear waste management by utilizing integrated data analytics and an
accelerated simulation architecture to address the complex non-linearities of large-scale
treatment processes. Powered by the Genesis Mission supercomputing resources and
synthesized from over 30 years of unparalleled EM multi-modal data—including bench-
scale experiments, engineering designs, and real-time instrumentation from SRS and
Hanford—the platform will learn transferable representations to predict scale-dependent
behaviors across lab, pilot, and full-scale systems. This predictive framework will
validate chemical flowsheets and optimize throughput to meet critical treatment
milestones, effectively creating a closed-loop cycle that compresses decade-long
deployment timelines.
C. Treatment Process Optimization (EM-3.2, ASCR)
INDUSTRY COST SHARE: Required (Up to 50% cost share with industry partners,
including in kind, depending on specific scope of work the partner will perform)
Implement AI-driven optimization of waste treatment facility throughputs to achieve
completion milestones at EM sites such as SRS and Hanford. This focus area aims at
optimization and/or real-time operation of dynamic process controls (e.g., for tank waste
treatment flowsheets, groundwater remedial systems, informing conditions for
decontamination and decommissioning, and nuclear material separation or isotope
recovery). Optimization and adaptive control of dynamic process systems – particularly
when limited, sparse, and heterogeneous training data are available – are vital to ensuring
safe, efficient, and resilient operations, especially for real-time decision-making or
support. This includes the use of AI-driven surrogate models, reinforcement learning,
and hybrid physics-data frameworks, and the development of digital twins that assimilate
sensor data from specific sites for scenario testing, early warning, and optimizing
remediation processes, waste disposition, and facility operations under uncertainty.
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7 - Discovering Quantum Algorithms with AI
Participating Offices: Office of Science-Advanced Scientific Computing Research (SC-
ASCR); SC-Basic Energy Sciences (SC-BES); SC-High Energy Physics (SC-HEP); SC-Nuclear
Physics (SC-NP); SC-Fusion Energy Sciences (SC-FES)
Challenge: Discovering new quantum algorithms is an exceptionally difficult challenge
due to the number of potential quantum operations and is highly counter-intuitive for human
researchers to navigate. U.S. leadership in the emerging quantum computing revolution will
require accelerating the design and development of quantum algorithms (including those that
capitalize on the convergence of classical HPC, AI, and quantum computers) that demonstrate
scientific utility and a provable quantum advantage.
AI Solution: Novel AI could discover new quantum algorithms by automating and
optimizing their design and translating them into applications without requiring prior domain
knowledge. Furthermore, AI-powered platforms can translate high-level problem descriptions in
natural language into executable quantum circuits, making algorithm design more accessible to
researchers from various fields. AI could help establish scientific workflows that leverage the
interplay of classical and quantum resources, managing data flow and executing complex
computations across platforms.
Justification: There is strong evidence that quantum computers and algorithms will
offer solutions to computational problems with high impact to the scientific community, beyond
the limits of classical HPC and AI. DOE hosts the most complete suite of scientific computing
capabilities and these advances in quantum capability will enable computations that are
classically intractable.
National Impact: The discovery of new quantum algorithms will have broad
applications to science domains, such as fusion sciences, high energy physics, nuclear physics,
materials science, and chemistry, with proposed commercial applications for the acceleration of
drug, material, and chemical discovery. This technological leap would not only bolster the
Nation's economy and security but also provide tools to address some of the most challenging
scientific and societal problems.
Focus Areas for FY 2026:
A. Application-aware Error Correction (ASCR)
Use AI to find efficient error correction for specific scientific applications and co-
design algorithms and hardware to identify and correct the errors that significantly impact
the final scientific result.
B. Computational Tools for Fault Tolerant Quantum Computational Science (ASCR)
Use AI and formal verification methods to develop novel, reduced complexity
quantum algorithmic primitives and compilation tools that can be used to deliver
scientific quantum advantage.
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C. Hybrid Quantum-Classical Optimization Algorithms (BES)
Use agentic AI workflows in quantum chemistry and materials sciences beyond
iterative parameter space searches for variational solvers across platforms, including
distributed execution on application needs. Use AI driven surrogate models to accelerate
quantum time evolution and many-body simulations by learning efficient representations.
D. Quantum Algorithms for Nonlinear Plasma Physics (FES)
Develop quantum algorithms suitable for nonlinear plasma physics problems and
apply them to facilitate configuration development and optimization of fusion devices.
E. Quantum Advantage for Nuclear and Hadronic Systems (NP, HEP)
Use AI to determine the quantum computation advantage in nuclear and hadronic
systems typically described through lattice quantum chromodynamics. Enable solutions
using quantum algorithms that demand AI to tackle the challenges of chiral symmetry
breaking, confinement, relativistic kinematics, and infinite degrees of freedom.
8 - Realizing Quantum Systems for Discovery
Participating Offices: Office of Science-Basic Energy Sciences (SC-BES); SC-High
Energy Physics (SC-HEP); SC-Advanced Scientific Computing Research (SC-ASCR); SC-
Nuclear Physics (SC-NP)
Challenge: Quantum systems for quantum computing, quantum sensing, and quantum
communication are poised to revolutionize discovery science by enabling unprecedented
capabilities in modeling, simulation, and measurement. The use of current quantum systems is
severely limited by their fragility, scalability, and integration into existing infrastructure. The
inherent challenge is the lack of understanding and control of the complex cause-and-effect
relationships within a quantum system.
AI Solution: AI has shown its ability to process vast amounts of multimodal data, to
recognize complex patterns and relationships, and to learn adaptively how to handle dynamic and
unpredictable environments. This ability makes AI uniquely suited to help understand and
control the delicate nature of complex quantum systems. In quantum computing, AI will assist
in real-time noise mitigation, adaptive error detection and correction, and system optimization
and design. In quantum sensing, AI will optimize quantum entanglement, increase sensitivity,
and control multi-sensor quantum networks. In quantum communication, AI will dynamically
optimize multi-node quantum network design and control. New AI approaches are needed for
making real-time decisions under quantum uncertainty, learning control policies when
observation is costly and destructive, predicting behavior from incomplete information, and
adapting to drift in real-time.
Justification: DOE is home to five National Quantum Information Research Centers in
the U.S. working on overcoming limitations in quantum computing, quantum sensing, and
quantum communication. The existing ecosystem is ideally suited to develop AI solutions to
enable full quantum control.
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National Impact: The AI enabled understanding and control of complex quantum
systems will accelerate the deployment of quantum technologies and accelerate their utilization
for currently intractable challenges in scientific discovery and technology development.
Focus Areas for FY 2026:
A. AI for Quantum Systems Design (BES)
Develop and apply AI frameworks to uncover the underlying causal relationships
that influence the performance of quantum systems to develop improved designs and
refined processes for the synthesis and fabrication of these devices.
B. AI for Control of Quantum System (HEP, NP)
Implement AI-based methods in control systems to dramatically improve the real-
time control of practical quantum systems, including automating and optimizing
calibration, tuning, noise mitigation, error correction, and readout processes, providing
accurate, practical, and scalable quantum control to a wide variety of potential users.
C. AI for Quantum Imaging and Sensing (HEP, NP)
Integrate AI into multi-qubit quantum sensing applications for extreme sensitivity
in both the laboratory and field environments to enhance and optimize the design,
fabrication, and operation of sensors based on the quantum properties of superconductors,
semiconductors, atoms, or other physical substrates.
D. AI for Quantum Computing and Networking (ASCR)
Leverage AI to mitigate decoherence in qubits, develop and implement effective
quantum error correction codes, control quantum processing units, and ensure the
scalability of quantum processors and network systems.
9 - Recentering Microelectronics in America
Participating Offices: Office of Science-Basic Energy Sciences (SC-BES); SC-
Advanced Scientific Computing Research (SC-ASCR); Office of Critical Minerals and Energy
Innovation-Advanced Materials and Manufacturing Technologies (CMEI-AMMTO); SC-High
Energy Physics (SC-HEP); SC-Fusion Energy Sciences (SC-FES)
Challenge: Microelectronics powers all aspects of our lives, including AI, but America
faces intense global competition in critical microelectronics applications, including ultra-energy-
efficient semiconductors for AI computing, power electronics, and communication networks.
Microelectronics faces a tremendous scientific and technological challenge: designing and
engineering the next generation of Beyond Moore microelectronic devices and platforms that
push the boundaries of miniaturization, processing speed, power consumption, thermal
management, and operations environment.
AI Solution: An AI-driven full-stack co-design ecosystem will enable faster innovation
cycles, de-risk new ultra-efficient manufacturing processes and component designs, and
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accelerate materials and manufacturing R&D, thereby leapfrogging foreign semiconductor
technology. Frontier AI coupled with heterogenous and multiscale data that is accessible via
federated learning techniques will accelerate microelectronics research by revealing the critical
relationships and tradeoffs between materials, devices, and workflows.
Justification: For decades, DOE has been at the leading edge of microelectronics
research, both as a consumer and as an engine of scientific innovation with expertise in advanced
materials, nanofabrication, and quantum-related technologies such as cryogenics, enabling many
of the technological breakthroughs adopted by industry.
National Impact: Microelectronics continues to be at the heart of technological
innovation, and every American will benefit from recentering leadership of the industry in the
U.S. This effort will ensure sustained U.S. leadership in the global semiconductor landscape,
enabling the rapid growth of domestic data centers, advancing beyond Moore's law for AI
computing and national security applications, and securing other technological advantages—such
as global leadership in 6G communication networks essential for economic prosperity and
national security.
Focus Areas for FY 2026:
A. Angstrom (sub-1-nm) Scale Microelectronics Manufacturing (AMMTO)
Accelerate lab-scale approaches for beyond-Moore and beyond-EUV and other
Angstrom-scale fabrication for advanced node logic through AI-enabled nano and
microfabrication. Of particular interest are approaches that enable greater design
flexibility than current advanced node technology.
B. Materials and Architectures for Non-von Neuman Computing Devices (BES)
Leverage AI to accelerate research on emerging organic and inorganic material
candidates, including 2D materials and materials exhibiting quantum effects or
properties, to advance radically different computational paradigms and overcome
bottlenecks in efficiency and performance.
C. AI-Driven Architecture Design (ASCR)
Develop and apply AI-based methodologies to model, prototype, integrate
hardware and software development in the design of next-generation computing
architectures, enabling optimization of complex hardware/software interactions, multi-
scale systems modeling, and identification of highly efficient, application-specific
computing architectures.
D. 3D non-volatile compute-in-memory technology (ASCR)
Utilize AI-driven codesign to revolutionize research, development, and
prototyping on emerging technologies and materials and enable back-end-of-line
integration of 3D non-volatile memory for electronic, optoelectronic, or photonic
computing applications.
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E. Physics-Based Circuit Design, Simulation, and Emulation (ASCR)
Leverage AI to link material composition, geometry, and operating conditions to
defect evolution and device performance, accelerating time consuming and
computationally intense processes and enabling non-intuitive optimization and greater
design flexibility for physics-based circuit design, simulation, and emulation, including
electronic design automation (EDA).
F. Microelectronics in Harsh Environments (HEP)
Integrate AI into the design and validation of robust devices and facilitate the
efficient operation of devices that process high data volumes in harsh environments
where there is no possibility of replacement or service.
G. Plasma-Enabled Microelectronics Manufacturing (FES)
Leverage AI to revolutionize the design, control, and optimization of plasma-
based processes for manufacturing advanced microelectronics that push the boundaries of
feature size and geometry and accelerate the discovery and implementation of optimal
and repeatable plasma synthesis conditions.
H. Power Electronics and Communication Networks (ASCR)
Deploy AI-driven design and manufacturing approaches for advanced (e.g., wide
bandgap and ultra-wide bandgap) power electronics and advanced communication
network (e.g. 6G and advanced wireless) technologies.
I. Low-temperature Electronics for Sensors and Computation (ASCR, HEP)
Low-temperature (mK up to 120K) electronics, including cryogenic CMOS and
superconducting logic, promises efficient, high-speed, and low-cost computing to address
Genesis Mission needs in edge computing and AI accelerators, but bottlenecks in design,
density, scaling, fabrication, and integration have prevented practical realization of these
benefits. Research applications may focus on EDA for superconducting digital logic,
analog and digital superconducting electronics for sensors or for classical control of
quantum computers, superconducting neuromorphic, AI, and control circuits, distributed
superconducting computing architectures, or cryogenic CMOS.
J. Transform Neuromorphic Computing Connectivity, Communication, and System
Hardware Integration (ASCR)
Significant connectivity and communication challenges exist in tackling practical
integration of neuromorphic computing hardware at scale and with instrumentation for
scientific computing. Multi-scale connectivity, relevant circuit motifs from connectome
data and efficient encoding schemes should be leveraged and appropriately translated to
neuromorphic circuits. Advances in this critical domain will enable the design of high-
bandwidth and massively parallel connectivity across emerging neuromorphic processing
units.
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10 - Securing U.S. Leadership in Data Centers
Participating Offices: Office of Critical Minerals and Energy Innovation-Industrial
Technologies Office (CMEI-ITO); Office of Electricity (OE); Office of Science-Advanced
Scientific Computing Research (SC-ASCR)
Challenge: Winning the AI race will require accelerating the process of developing and
deploying new data center technologies and energy management strategies to provide the
extreme compute power for AI advancements, while ensuring secure, reliable, and affordable
energy for consumers.
AI Solution: By leveraging AI, digital twins, and cyber-physical testbeds, we can
rapidly de-risk advanced data center technologies and their grid integration, accelerating the time
to deployment and supporting stakeholder needs including data center operators, equipment
providers, communities, and utilities. AI can accelerate physics-based models to enable real-
time digital twins, explore millions of deployment scenarios, and optimize under a unique
constraint surface.
Justification: This project leverages DOE's capacity to convene data center and utility
stakeholders, the DOE/NNSA National Laboratories’ research expertise in both load flexibility
and computing, and their unique cyber-physical testbed facilities. Specifically, the DOE already
supports the Center of Expertise for Data Center Energy at Lawrence Berkeley National Lab,
which can provide resources, including a variety of data sets on data center energy use, to aid
this effort.
National Impact: By ensuring a robust capacity to develop cutting edge data center
technologies and load management strategies, we will solidify U.S. intellectual and economic
leadership in AI, driving prosperity and security while maintaining secure, reliable, and
affordable energy for consumers.
Focus Areas for FY26 and 27
A. Data Center Load Flexibility (ITO)
Develop and demonstrate AI models that can analyze, design, and orchestrate
smart, grid-aware load flexibility techniques, such as software-level workload
management, infrastructure-level cooling modulation, onsite generation or storage
resource activation, or innovative power electronics technologies, to transform the
operations of data centers by enabling them to be responsive to real-time grid conditions.
Proposed approaches should have the ability to assess load flexibility interventions, both
independently and in tandem, through scenario modeling, and to, ultimately, reduce data
center peak demands, stabilize load fluctuations, mitigate harmonics, or demonstrate load
shifting while maintaining reliable access to power for intensive compute applications.
Approaches that are generalizable to other large loads are encouraged.
B. Data Center Thermal Management (ITO)
Apply AI-based tools and approaches to advance the state-of-the-art in data center
thermal management technologies at the chip-, rack-, and facility-level that reliably
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enable peak performance and efficient operation for current AI and computing chipsets
and future higher-powered chipsets. The proposed approaches should demonstrate
improvements in cooling efficiency in data centers and/or show promise of reductions in
data center power and direct and indirect water use.
11 - Achieving AI-Driven Autonomous Laboratories
Participating Offices: Office of Science-Basic Energy Science (SC-BES); SC-
Advanced Scientific Computing Research (SC-ASCR); SC-Fusion Energy Sciences (SC-FES)
Challenge: The pace of scientific discovery is fundamentally constrained by the
traditional, human-driven experimental process and the availability of non-deterministic AI-
driven control tools to implement complex experimental designs in combinatorially large design
parameter spaces. These bottlenecks slow the cycle of hypothesis, experimentation, and
discovery, leading to inefficient use of critical national assets and delaying scientific
breakthroughs. Automating at least some parts of the scientific experimental scheme will both
increase the volume of data produced for improved AI models and improve the repeatability of
experiments.
AI Solution: Artificial intelligence will be integrated directly into the experimental
workflow and data analysis, combining robotics, edge AI, real-time analysis and intelligent
feedback, hypothesis generation, and data curation/sharing.
Justification: These AI-driven laboratories will allow scientists to explore complex
phenomena at an unprecedented rate and scale and are critical to achieving the Genesis Mission
goals. DOE’s user facilities and long-standing national laboratories have the infrastructure,
capabilities, and expertise to serve as the nucleus for innovation with this type of high throughput
discovery.
National Impact: Accelerating discovery through AI-driven laboratories will directly
advance U.S. scientific leadership and economic competitiveness. This capability will speed up
the development of novel materials and molecules for energy, next-generation computing,
national security, and biotechnology. Like other challenges, it will also solidify the Nation's
position at the forefront of AI and scientific innovation, create a new paradigm for 21st-century
research, and train a future workforce fluent in the integration of AI, data science, and
experimentation.
Focus Areas for FY 2026:
A. Advanced Robotics for Dynamic Laboratory Environments (ASCR)
Develop the foundational computational and algorithmic capabilities required for
robotics and other embodied AI systems to navigate and perform tasks within these
unstructured, complex settings, and tying them to high-performance computing systems
and AI supercomputers. This involves creating novel AI and applied mathematics
frameworks for real-time perception, path planning, and manipulation under uncertainty,
creating digital twins to train AI, optimize designs, and, ultimately, reduce the cost of
scientific discovery. Building upon recent advances in humanoid and other multi-
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purpose robotics systems, develop control algorithms that enable robots to safely interact
with scientific instruments and manage experimental workflows for high-risk/low-
throughput and repetitive tasks, improving time-to-result and reducing human
intervention The goal is to establish a robust, cross-cutting software and algorithmic stack
that can be broadly deployed across the DOE/NNSA laboratory complex, transforming
research facilities into more adaptive and efficient discovery platforms with reusable
capabilities that generalize across SC and DOE priority areas and DOE HPC platforms.
B. AIOps - AI for Network Operations (ASCR)
Tools and technologies are needed that enable the use of AI for research network
operations (AIOps) to maintain reliability and resilience in autonomous laboratory
settings while increasing capacity and capability to support AI-driven science. The
Applicant is encouraged to include domain science expertise in the proposed team. The
proposed solution must be demonstrated within the first project phase to scale for use in
more than one of the science domains that SC Programs steward and can address one or
more of the following: 1) AI-enabled curation of multi-modal network operations data,
making these data ready for use in AIOps tailored to the priorities of high-performance
research networks that support complex workflows and may engage multiple
experimental and observational scientific user facilities as well as modeling and
simulation data. The solution must include tagging of science project information to
enable domain science-aware network operations. must enable correlation of data that
describe the same research network event, even if derived from different data sources,
and must ensure appropriate protection of sensitive data. 2) AI-driven predictive analysis
that anticipates the network requirements of multi-facility integrated science workflows
to inform the workflow initial design and identify and implement real-time proactive
network mitigations that ensure domain-science specific network performance
requirements remain satisfied while the workflow executes. The solution must identify
research network service interdependencies to characterize the wider effects of local
network changes or component failures, and must detect, and conduct root-cause analysis
of, anomalous network behavior encountered during workflow execution.
C. AI-Accelerated Science: Correlation to Understanding (BES)
The power of AI could be extended beyond automated data collection and
processing, identification of correlations, and optimization of study variables to enable
rapid, autonomous generation of scientific knowledge from integrated data. Key to
transforming the pace of basic energy science is the high priority goal to demonstrate
AI-enabled identification of phenomena or correlations in high-throughput data that are
not consistent with or otherwise predicted by current scientific models and generation
of a plausible new explanation as a testable hypothesis in a truly closed-loop process for
advancing science. AI-assisted identification of scientific gaps and subsequent
hypothesis generation would enable an optimization of the research direction and amplify
the increased pace of scientific discovery possible from automation of laboratory tasks
alone.
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D. AI-Enabled Diagnostics and Remote Handling (FES)
Remote handling of irradiated wall material, maintenance of diagnostics, and
replacement of tiles can be performed with AI-driven robots. Feedback control will be an
integral part of maintaining a fusion power plant as edge AI can rapidly accelerate the
data processing, stability analysis, and prediction of performance required to confine
plasmas, minimize damage to the experiment, and deliver stable reactor conditions.
E. Accelerate the design and prototyping of neuromorphic computing circuit
primitives for robotic embodied physical artificial intelligence (ASCR)
Revolutionizing neuromorphic computing requires understanding the
computational components and principles, e.g., neurons, dendritic trees, and local
plasticity, that underpin the brain’s functionality and robustness for performing
autonomous tasks. These biological primitives must be translated into functionally
equivalent circuits that could be implemented, validated, and embodied within
autonomous robotic computing systems that can learn and adapt to perform tasks. Novel
neuromorphic circuits based on current and emerging technologies guided by
neuroscience-inspired functionality need to be engineered to realize high-performing
computational systems.
12 - Designing Materials with Predictable Functionality
Participating Offices: Office of Science-Basic Energy Science (BES); SC-Advanced
Scientific Computing Research (SC-ASCR); SC-Fusion Energy Sciences (SC-FES); SC-Isotope
R&D and Production (SC-IRP); Office of Critical Minerals and Energy Innovation-Advanced
Materials and Manufacturing Technologies Office (CMEI-AMMTO); CMEI-Industrial
Technologies Office (CMEI-ITO); CMEI-Alternative Fuels and Feedstocks Office (CMEI-
AFFO)
Challenge: Accelerating materials innovation will enable rapid deployment of advanced
energy and industrial technologies that are essential for American competitiveness, from
structural materials to materials for energy storage to other functional materials for advanced
technologies. The identification and commercialization of new materials with transformative
properties that dramatically improve performance, energy efficiency, reliability, and resilience,
however, is a time- and resource-intensive process due to the inherent complexity of materials
science and the practical limitations of traditional simulations, synthesis, and characterization
techniques that still require significant trial and error.
AI Solution: The convergence of current and emerging AI technology with the growing
availability of large, curated datasets may be a tipping point for materials discovery, design, and
qualification. The development of physics-aware AI frameworks that exploit the complementary
strengths of foundation models, deep learning, computer vision, generative AI, and agentic AI
will enable entirely new capabilities for materials design that iteratively couple prediction,
synthesis, characterization, and analysis to yield closed-loop learning systems that are
interpretable, trustworthy, and capable of bridging large scales in space and time. The ultimate
goal of inverse design (designing materials for given property specifications) requires advanced
experimental and simulation capabilities as well as AI reasoning and explainability.
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Justification: DOE’s suite of world leading and unique experimental and computational
capabilities for materials research, including X-ray light sources, neutron scattering facilities
(and their associated characterization equipment), nanoscale science research centers, materials
databases, and exascale computers, is collectively the most comprehensive and performant in the
world. These capabilities, along with the availability of very large materials data sets coupled
with sustained investments in the development of AI-enabled physics-informed models, has
positioned DOE to take a leadership role in implementing the materials by design vision.
National Impact: Tight integration of AI into the materials discovery-to-product
workflow could significantly reduce time to market in manufacturing—from many years to
decades down to months to a few years. This acceleration will dramatically reduce development
timelines for critical technologies including batteries, energy systems, structural and functional
materials, strengthening American technological leadership and enabling faster deployment of
innovations that create jobs and strengthen economic and national security.
Focus Areas for FY 2026:
A. Functional to Quantum Materials (BES)
Develop physics-aware AI frameworks and foundation models necessary to orchestrate
high-throughput computation, high-fidelity simulations, and automated synthesis and
characterization to enable fundamental understanding of functional, quantum and emergent
properties on the atomistic and electronic level to predict and realize specific physical
properties that are currently inaccessible but required to advance lossless power
transmission, energy-efficient computing, advanced sensing, ultrafast switches, and
quantum devices.
B. Structural Materials (BES, FES, AMMTO)
Develop interpretable AI models and agentic experimental workflows, grounded in
advanced computational tools and scientific methods, to discover, understand, and
manipulate the complex interactions governing materials behavior under stress and other
extreme environments and to enable controlled design of mechanical behavior.
C. Biomolecular Materials (BES)
Establish AI frameworks that integrate atomic and molecular modeling across scales and
bridge biological and chemical data to realize a “design to order” paradigm for new
biological molecules and hierarchical structures suited to extreme environments.
D. Plasma-Facing Materials (FES)
Deploy AI-enabled digital twins of heat and plasma exposure facilities to support the
characterization of physical and mechanical properties, manufacturing, and qualification of
materials that directly interact with fusion energy plasmas and face some of the most
extreme temperatures, neutron fluxes, and environmental stresses on Earth.
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E. Targetry by Design (IRP)
Establish AI-driven “Targetry by Design” to accelerate breakthroughs in isotope
production, enrichment, and chemical separation technologies, leading to an expanded
portfolio of domestically produced isotopes and securing a reliable U.S. supply chain for
energy, computing, medicine, and national security.
F. AI-Enabled Materials Discovery, Development, and Qualification (AMMTO)
Deploy automated discovery platforms to accelerate identification and design of novel
manufacturable energy materials across critical materials, high-temperature structural
materials, semiconductor materials, and energy storage materials. Develop closed-loop
autonomous systems that integrate hypothesis generation, experimental validation, and
manufacturing process through AI-driven frameworks. Implement rapid prototyping and
characterization paired with AI-enabled prediction to compress traditional multi-year
qualification processes.
G. Electrochemical Energy Conversion Catalyst Discovery and Scale up (AFFO)
This topic solicits projects using AI to accelerate the search for platinum group metal
(PGM) free oxygen reduction catalysts for electrochemical energy conversion devices. The
current state of the art materials require multi-step synthesis routes often based on the
pyrolysis of metal organic framework materials typically followed by metal substitution(s)
to yield atomically dispersed metal sites in carbon frameworks. These synthesis routes are
intricate, time-consuming, and difficult to control -often yielding differing electrochemical
performance despite ‘identical’ syntheses and making material performance prediction
difficult prior to synthesis and testing and manufacturing/scale-up high-risk. This topic
seeks applications focused on materials performance prediction and AI-guided materials
synthesis to both improve repeatability for existing catalysts and to develop new, higher
performance PGM-free oxygen reduction catalysts. Applications are expected to be
primarily lab led with IHE participation.
13 - Enhancing Particle Accelerators for Discovery
Participating Offices: Office of Science-High Energy Physics (SC-HEP); SC-Basic
Energy Sciences (SC-BES); SC-Nuclear Physics (SC-NP); SC-Fusion Energy Sciences (SC-
FES); SC-Isotope R&D and Production (SC-IRP); SC-Advanced Scientific Computing Research
(SC-ASCR)
Challenge: Modern particle accelerators are complex, requiring extensive human
intervention that leads to high operating costs, operational variability, suboptimal experiment
optimization, and inadequate data integration. Further, the physical limitations of existing
accelerator technologies slow progress in pushing the limits of resolution in space, time, and
energy. Transforming accelerator-based facilities into highly efficient, autonomous, and more
productive capabilities requires a tight integration of AI with design and operation.
AI Solution: Predicting chaotic beam dynamics, in which small perturbations cascade
into major problems, could push AI to develop new capabilities in multi-scale temporal
reasoning, physics-constrained learning, and robust uncertainty quantification. AI-driven digital
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twins that simulate complete beam dynamics in real time could dramatically reduce tuning time.
Collectively, facility-based AI will become adaptive and self-updating, significantly boosting
performance, efficiency, and scientific output.
Justification: DOE stewards one of the largest suites of accelerator-based experimental
facilities in the world, with extensive operational data and a large, highly skilled workforce. The
optimization of the Nation’s large-scale scientific infrastructure through AI-enabled design and
the elimination of operational bottlenecks and cost inefficiencies will maximize the Nation’s
return on current and future infrastructure investments, revealing entirely new paradigms for
scientific research through human-AI teaming and accelerating discovery.
National Impact: Accelerator-based facilities have been central to many of the most
important discoveries of the 20th and 21st centuries. Integrating AI into accelerator design and
optimization will increase the pace of future breakthroughs, enabling a deeper understanding of
the universe, the development of new energy and computing technologies, and the creation of
new techniques for the diagnosis and treatment of disease.
Focus Areas for FY 2026:
A. AI-driven Accelerator Facilities (BES, HEP, IRP, NP)
Enable and deploy AI systems that provide real-time operational advice, automate
control functions, enhance beam stability, reduce beam tuning time, predict equipment
failure, detect faults, and optimize performance for both large and small-scale accelerator
facilities currently operating or under construction. Scope includes the development and
deployment of high-fidelity AI-driven "digital twins" of these particle accelerators to
enable a sophisticated simulation and design environment and AI systems that can
mitigate cost and risk of accelerator facilities under construction.
B. Integration of Digital Twins for Fusion Systems and Actuators (FES)
Establish real-time control of nuclear fusion systems and facilities, leveraging the
massive concurrency of experimental data and high-fidelity simulation results to monitor
stability and provide feedback control for critical fusion technology for tokamaks,
stellarators, and inertial fusion devices
14 - Unifying Physics from Quarks to the Cosmos
Participating Offices: Office of Science-High Energy Physics (SC-HEP); SC-Nuclear
Physics (SC-NP); SC-Advanced Scientific Computing Research (SC-ASCR)
Challenge: The universe obeys only one set of rules, and scientists have hundreds of
experiments targeting parts of that one set. Experiments range over distance scale (31 orders of
magnitude), cost ($100k to >$1B), duration (fractions of a second to decades), and human
investment (few to thousands). We need a way to integrate the disparate experimental results
with theoretical knowledge to accelerate discovery.
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AI Solution: High energy physics and nuclear physics form a unique foundation to build
AI reasoning models at unprecedented scale. Developing AI that simultaneously learns from
particle collisions, nuclear decays, and cosmological surveys will require breakthroughs in multi-
modal learning and the ability to derive insights rather than merely recognize patterns. An AI
that internalizes the Standard Model could accelerate analysis by orders of magnitude, identify
anomalies pointing to new physics, and propose theoretical extensions consistent with all data - a
leap from pattern matching to physics reasoning.
Justification: DOE uniquely possesses the confluence of world-class scientific talent,
stewardship of cutting-edge facilities, unparalleled access to experimental data, and a critical
national mission to effectively address this challenge. These facilities, alongside a broad
portfolio of programs that explore the fundamental constituents and forces of the universe and
delve into the nature of atomic nuclei, underscore DOE's singular ability to conduct large-scale,
long-term, and high-impact scientific research.
National Impact: The acceleration of discovery, particularly in areas involving vast
datasets from cutting-edge experiments, means we could reach breakthroughs much faster than
previously possible, impacting our technological capabilities and quality of life in unforeseen
ways. Questions such as "Why is there more matter than antimatter?" "What is the nature of
dark matter?" and “How do protons generate mass and spin?” address the nature of reality, and
answering questions through advanced AI could have profound shifts in our philosophical and
scientific understanding of the cosmos and our place within it.
Focus Areas for FY 2026:
A. Foundation Models of Particle Interactions and Cosmic Physics (HEP, NP):
Develop and curate the essential data of nuclear and particle experimental efforts,
critical to train foundation models of particle interactions and cosmology to accelerate
new breakthroughs in our understanding of the universe. Data and models may include
the future Electron-Ion Collider, cosmic observations, underground and accelerator-based
experiments as well as synthesizing different modalities of data from across multiple
large-scale sky surveys to understand nuclear astrophysics, dark energy, dark matter, and
the physics of the early Universe. Successful scope will seamlessly span experimental
and theoretical inputs across the pinch points of analysis pipelines from detector-level
through to final scientific artifacts, along with the output of advanced theoretical
calculations. Discovery science potential will be maximized by addressing such technical
challenges as sparse-data domains and real-time data acquisition of high-dimensional
petabyte-scale datasets with associated scalability challenges and interpreting the
experimental signals using theoretical knowledge.
B. AI Accelerated DUNE Science (HEP)
Develop AI methods that significantly speed up and enhance the DUNE science
program, reducing the time needed for the collaboration to publish neutrino oscillation
measurements, significantly improving the sensitivity to neutrinos from core-collapse
supernova, and developing new flagship measurements that will enhance the DUNE
science goals.
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C. Expedited Discovery from High Complexity and Petabyte-Scale Datasets (HEP, NP)
Partner domain expertise with data science and industry to develop AI methods
and techniques capable of drawing robust scientific insight from increasingly complex
and/or petabyte-scale datasets. Enable deeper insights by directly connecting datasets
with theoretical parameters for uncertainty-aware reasoning to leverage the high-
dimensionality of particle physics datasets. Scope will address the critical slowdown
problem in Lattice QCD, automate big-data analysis, achieve new levels of experimental
precision and theoretical calculation, and significantly improve understanding of the
universe and particle interactions. AI-assisted design to maximize experimental
sensitivity to fundamental parameters of interest or that significantly reduces costs of
proposed or current projects and can be implemented in the next three to five years, is
also of interest.
15 - Predicting U.S. Water for Energy
Participating Offices: Office of Science-Biological and Environmental Research (SC-
BER); SC-Advanced Scientific Computing Research (SC-ASCR); Critical Minerals and Energy
Innovation – Integrated Energy Systems Office (CMEI - IESO)
Challenge: Water availability is essential for expanding production and utilization of
energy, as well as the Nation’s health and security. However, there are fundamental scientific
gaps in our understanding of terrestrial and atmospheric systems that limit our ability to predict
water resources, especially on the time scale of weeks to years.
AI Solution: AI capable of multi-scale temporal reasoning could tackle three inter-
related grand challenges: cloud physics, surface and subsurface water flows, and the broader
hydrologic cycle. AI could improve, accelerate, and couple exascale-class modeling systems
through advances in model initialization, and develop surrogates trained on DOE’s atmospheric
and terrestrial observations and laboratory data, at a fraction of the computational cost of existing
models. AI-based model diagnostics for enhanced analysis could refine a model-observational
system aligned with decision-making needs.
Justification: DOE is the only agency with AI expertise, advanced computing, and
integrated modeling capabilities (e.g., the DOE Energy Exascale Earth System Model, or
E3SM), and infrastructure for field research necessary to meet the challenge of providing
accurate information on surface and ground-water availability on the time scales of weeks to
years.
National Impact: Solutions to these longstanding science challenges will radically
improve America’s ability to anticipate water supply in the context of changing water
availability, demands, energy technologies, and ambitions for energy expansion.
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Focus Areas for FY 2026:
A. Cloud Microphysics and Atmospheric Turbulence (BER, IESO)
Develop and apply AI capabilities that integrate theory, field observations, and
laboratory research to represent complex non-linear multi-scale interactions governing
atmospheric microphysics and turbulence that are important to the formation, phase, and
intensity of precipitation. This topic includes development of physics-constrained AI-
enabled models that address current challenges in prediction of cloud and precipitation
microphysical processes, precipitation-relevant atmospheric aerosol processes,
atmospheric turbulence, and/or cloud dynamics; cutting-edge AI-enabled laboratory
experiments in microphysics and turbulence; and application of novel AI-driven analysis
techniques to extract undiscovered insight into microphysics, precipitation, and
turbulence in existing atmospheric field data sets.
B. Water and Energy (BER)
The predictive understanding of surface and groundwater is crucial for ensuring
sufficient water for energy production and for protecting energy infrastructure from
floods. The core scientific objective is to use advanced AI techniques to create a coupled
surface-groundwater model that improves hydrologic process understanding and informs
prediction of water availability. Topics of interest include the creation of integrative
models that utilize data of varying levels of complexity including multi-source
observational data and high-resolution model outputs; the advancement of a hierarchy of
models and multi-modeling capabilities ranging from process-based models to
Foundation models; the development of transferable, hybrid modeling capabilities so that
advances in one region can be translated to another; and robust model evaluation
capabilities. The applications to this research area must incorporate use cases to develop
and test a new integrative framework focused on regional energy needs and flood
resilience.
C. Weeks to Years Prediction (BER)
Leverage the power of generative and explainable AI to accelerate, refine, and
analyze the DOE Energy Exascale Earth System Model (E3SM) and complementary
data-driven and hybrid AI models to provide skillful regional predictions of water at the
seasonal to multi-year time scales needed to inform energy system planning and
operations. Topics of interest include development of AI-driven techniques for coupled
model initialization, development of hybrid AI-physical models to improve process
components of coupled models or nonlinear natural modes of variability and associated
teleconnections that impact precipitation, coupling of E3SM and foundation models, and
AI emulators and downscaling techniques to produce hyperlocal information for domestic
energy providers.
16 - Scaling the Grid to Power the American Economy
Participating Offices: Office of Electricity (OE); Office of Critical Minerals and
Energy Innovation (CMEI); Office of Science-Biological and Environmental Research (SC-
BER); SC-Advanced Scientific Computing Research (SC-ASCR)
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Challenge: The electric grid faces reliability challenges and infrastructure limitations as
it struggles to accommodate dramatic increases in electricity demand from data centers,
manufacturing, and electrification while maintaining affordable power for Americans.
AI Solution: AI, using deep and reinforcement learning techniques on newly integrated
big data sources, will reduce uncertainty, improve insights, and speed processes in grid planning,
interconnection, operations, and security. This effort aims to enable 20-100x faster decision-
making and at least 10% improvement in electricity cost and reliability.
Justification: The utility sector has critical grid data but a low risk tolerance, limited
ability to develop new technology, and regional focus. Technology suppliers have innovative
solutions but may lack access to robust operational technology test environments to integrate and
validate their systems. DOE can leverage integrated energy system expertise, computational
facilities, testbed infrastructure, and strong partnerships with grid operators to bring these
capabilities together and develop validated, deployable AI solutions for the grid.
National Impact: AI-enabled modernization will deliver more reliable power at lower
cost to American homes and businesses while strengthening energy security through faster
deployment of grid capabilities and improved resilience against threats.
Focus Areas for FY 2026:
A. Grid Modeling and Analysis (OE, CMEI-IESO, SC-ASCR)
INDUSTRY COST SHARE: 20% cost share for R&D activities/50% cost share for
demonstration activities. Submissions must include a demonstration component of at
least 20% of the effort.
Submissions under this focus area will leverage AI to improve modeling and
analysis to yield more comprehensive and robust tools for grid planners to understand the
impacts of different upgrades, interventions, or courses of action on grid behavior. Of
particular interest are applications focusing on:
(i) Developing and demonstrating a foundational power system model with self-
improvement capabilities to support AI-enabled applications for grid operations and
infrastructure planning. A grid foundational model is a holistic, multi-modal AI
representation of the entire electric power system, trained on vast and diverse datasets
including grid topology, real-time operational data, market dynamics, environmental
factors, and historical events. The model will learn, understand, and predict complex
grid behaviors, continuously refine its performance, adapt to new challenges, and
provide optimal insights and solutions for grid operations and long-term infrastructure
planning.
(ii) Developing and demonstrating AI-driven model copilot tools—for example, agentic
interfaces for rapid scenario analysis and system planning support to stage, execute,
and interpret analysis in conventional power flow software tools.
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Projects in this focus area should include rigorous statistical characterization of
uncertainties to enable more robust, risk-aware, and reliable grid management, which is
especially crucial with increasing stochasticity from dynamic loads.
ADDITIONAL PARTNERSHIP REQUIREMENTS: Submissions to this focus area must
include an electricity sector team member (either as a prime or sub recipient) that can
provide real data as part of the development, demonstration, and validation of the
proposed technology. The team member must be located in the United States and could
include, but is not limited to: rural electric cooperatives; utilities owned by a political
subdivision of a state, such as a municipally owned electric utility; utilities owned by any
agency, authority, corporation, or instrumentality of one or more political subdivisions of
a state; investor-owned electric utilities; regional transmission operators/independent
system operators; electric aggregators; or electric wire owning and/or operating entities.
B. Grid Operations Optimization (OE, CMEI-IESO, SC-ASCR)
INDUSTRY COST SHARE: 20% cost share for R&D activities/50% cost share for
demonstration activities. Submissions must include a demonstration component of at
least 20% of the effort.
Submissions in this focus area will use AI to fundamentally enhance the
operational efficiency, stability, and responsiveness of the grid. The applications should
develop and deploy AI-augmented measurement of grid dynamics, identify anomalies
and vulnerabilities, and provide advanced data analytics, situational awareness, and
decision support tools for operators, resulting in more optimized, reliable, and cost-
effective grid operations. Projects in this area should leverage real-time physical grid
data and OT network traffic to provide situational awareness of emerging grid conditions,
with embedded AI-enabled logic making real-time, optimized recommendations for grid
control, resource dispatch, and risk mitigation. These tools should implement
transparency to allow operators to understand and validate these recommendations.
ADDITIONAL PARTNERSHIP REQUIREMENTS: Submissions to this focus area must
include an electricity sector team member (either as a prime or sub recipient) that can
provide real data as part of the development, demonstration, and validation of the
proposed technology. The team member must be located in the United States and could
include, but is not limited to: rural electric cooperatives; utilities owned by a political
subdivision of a state, such as a municipally owned electric utility; utilities owned by any
agency, authority, corporation, or instrumentality of one or more political subdivisions of
a state; investor-owned electric utilities; regional transmission operators/independent
system operators; electric aggregators; or electric wire owning and/or operating entities.
C. Uncertainty Quantification (SC-BER, SC-ASCR, OE, CMEI-IESO)
Submissions to this focus area will investigate the sources of uncertainty in grid
planning and operational models, including multi-sector inputs, and seek to understand
how AI can expand or reduce those uncertainties. Examples include (a) external forcings
on the power grid and feedback loops in multi-sector energy models and (b) the impact of
differential privacy to protect data in federated learning models on outcome uncertainty.
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17- Unleashing Subsurface Strategic Energy Assets
Participating Offices: Office of Hydrocarbons and Geothermal Energy (HGEO); Office
of Science-Biological and Environmental Research (SC-BER); SC-Advanced Scientific
Computing Research (SC-ASCR); SC-Basic Energy Sciences (SC-BES)
Challenge: Delivering cost-effective energy from the Earth’s subsurface entails the use
of heterogeneous reservoirs, dominated by fractures. Tools capable of predicting reservoir
behavior and the interactions of complex biogeochemical, mechanical, and hydrologic processes
are critical to the development of innovative, cost-effective extraction of subsurface energy
sources, including unconventional oil and gas, geothermal, and coal bed methane.
AI Solution: Developing AI capable of reasoning under extreme uncertainty, integrating
heterogeneous data types (i.e., seismic, geochemical, biological, hydrologic), and building
predictive models of systems that cannot be directly observed has broad applicability to any
domain requiring inference from indirect evidence. For subsurface science, AI that connects
molecular-scale mechanisms to field-scale resource availability will transform the field from
descriptive to predictive. DOE’s vast biological, geochemical, geomechanical, and hydrologic
data sets can be combined with high performance modeling capabilities, laboratory analytics, and
a suite of AI technologies, including surrogate models, physics-informed machine learning, and
digital twins to enhance engineering evaluation and control of the subsurface during
characterization, drilling, stimulation, and production.
Justification: DOE’s laboratory complex includes a vast array of data, modeling, and
analytical capabilities (e.g., SMART multi-lab Initiative suite of tools and ML-based surrogate
models; NETL’s EDX Discover; LBNL’s TOUGH simulation suite; LANL’s GeoDTi design
tool; LLNL’s GEOS software; and PFLOTRAN and ATS models), that supports extensive
research and operational activities in subsurface environments, and are uniquely positioned to
apply AI to accelerate development of models that emulate microbial, mineral, and fluid
interactions across molecular to field scales. Efforts will assemble, train, and analyze
information from vast data libraries, experiments, and operational sensors, to produce subsurface
transport models and digital twins to enhance reservoir characterization, enable real-time
reservoir simulation, and address uncertainty to increase hydrocarbon resource recovery and
unlock geothermal resources.
National Impact: Understanding subsurface complexities and stored energy sources is
key to securing the Nation’s energy future and will reduce the costs of recovery for oil, gas, and
heat, reduce costs of power to U.S. consumers, re-shore manufacturing, and enhance U.S.
competitiveness.
Focus Areas for FY 2026:
A. Chemical and Hydrologic Transport in Subsurface (BER)
To improve understanding and prediction of subsurface processes, AI can be
utilized to incorporate vast amounts of data, including molecular-scale interactions and
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field-scale resource availability, with mechanistic and process-based predictive models of
hydrological, geochemical, ecological, and biological processes occurring from soil pores
to the subsurface/deeper bedrock. Responsive applications will use AI to incorporate
mechanistic and quantitative knowledge of molecular processes occurring in the
subsurface/rhizosphere/soil pores. DOE’s datasets, combined with modeling and AI
technologies will be crucial to enable development of a hierarchy of models that
incorporate microbial, mineral, and fluid interactions to enhance reservoir
characterization and resource recovery.
B. Evolution of Fractures in the Upper Crust (BES)
Develop and use AI to advance geophysical understanding of subsurface
processes that characterize fractures in the upper crust associated with enhanced
geothermal systems and hydrocarbon recovery.
C. Control of Subsurface Fractures (HGEO)
Use multivariate and multiscale data, high-performance computing, and machine
learning & AI to measure and evaluate the interaction of natural and induced fractures
before, during and after stimulation. Assess methods to rapidly analyze data from geo-
physical, mechanical, and chemical sensors with other field data and numerical models,
generating rapid predictions to support real-time decisions that will improve the recovery
factor and cost effectiveness of unconventional hydrocarbon and geothermal energy from
existing and marginal resources. Supported by the Office of Oil and Gas and Office of
Geothermal.
In addition to the Genesis Mission Science and Technology Challenges of National
Importance provided above, applications addressing one of the focus areas in the following
topics will be considered.
18 - HPC Code Curation, Translation, and Development for Accelerated Scientific
Discoveries
Participating Offices: Office of Science-Advanced Scientific Computing Research (SC-
ASCR), Office of Critical Minerals and Energy Innovation-Advanced Materials and
Manufacturing Technologies (CMEI-AMMTO)
Challenge: Modern scientific advancements are heavily reliant on large-scale modeling,
simulation, and analysis codes. The present development and porting of these complex
applications are protracted and labor-intensive, often requiring years and extensive teams of
computational scientists, applied mathematicians, and performance engineers. This inefficiency
diverts critical expertise from scientific innovation and hinders rapid response to national needs.
While commercial AI coding tools exist, they currently lack the trustworthiness, understanding
of scientific principles, and ability to produce verified, reproducible, and uncertainty-quantified
results necessary for robust scientific applications.
AI Solution: A transformative opportunity exists to leverage artificial intelligence to
fundamentally alter the software development process for high-performance computing (HPC).
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By learning from decades of DOE codes, compiler traces, and performance data, AI systems can
actively assist in generating, optimizing, and verifying scientific software. This AI-assisted
development will accelerate discovery, enable rapid response to emerging national needs, and
empower computational scientists to focus more fully on scientific inquiry rather than the
intricacies of code development. This involves fundamental advancements in programming tools
through innovations in multimodality, tool use, deeper reasoning and planning, memory, and
external interaction. The ultimate goal is to establish an autonomous scientific code
development platform that unifies reasoning-scale foundation models, neuro-symbolic agents,
compiler/autotuning models, and workflow orchestration systems to generate, port, and optimize
codes across diverse architectures. The proposed solution should be generalizable, provide a
proof of concept, validation, and scalability.
Justification: The Department of Energy has an extensive repository of scientific codes,
compiler traces, and performance data from its supercomputing facilities and research activities.
This rich dataset, combined with the expertise within DOE/NNSA laboratories (e.g., in
supercomputing systems, applied mathematics and algorithms, exascale applications, compilers
and performance tools), provides a unique foundation for training AI systems. These systems
will incorporate physics- and mathematics-informed foundation models to ensure numerical
correctness and reproducibility. By integrating these resources, DOE is uniquely positioned to
lead the development of AI that can transform the end-to-end process of developing and
optimizing HPC codes, making it more efficient and robust. This will maximize the return on
investment in leadership computing facilities and improve resilience to future hardware
evolution.
National Impact: Accelerating the development and optimization of HPC scientific
codes will drive breakthroughs across multi-scale physics, materials science, chemistry, fusion
energy, and biology by significantly reducing the time lag from theoretical concepts to scalable
simulations. This will enhance the nation’s scientific competitiveness, enable faster solutions to
critical energy and national security challenges, and establish a new paradigm of "AI-generated
computational science" widely applicable across disciplines.
Focus Areas for FY 2026:
A. AI-Driven Code Porting and Optimization (ASCR)
Develop and demonstrate AI systems capable of parsing existing DOE flagship
application codes, identifying performance bottlenecks, and automatically generating
optimized code for specific leadership-class computing facilities (LCFs), emphasizing
GPU optimization and other heterogeneous and accelerated architectures.
B. Automated Scientific Problem-to-Code Generation (ASCR)
Integrate AI capabilities to translate high-level scientific problem descriptions
into governing equations, select scalable numerical algorithms, and produce performant,
documented code for new scientific problems.
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[Document continues — 98 more pages]

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Sample OT and Project Agreements

Sample Other Transaction Agreements for
DE-FOA-0003612
The Genesis Mission: Transforming Science and Energy
with AI
Contents
Genesis Mission Funding Agreement – Milestones
Genesis Mission Funding Agreement – Reimbursement
IP Appendix II – Patent Rights – 401.14
IP Appendix II – Patent Rights – Simplified
IP Appendix III – Data Rights – Open
IP Appendix III – Data Rights – Proprietary
Genesis Mission Project Agreement – Data Use Agreement
Genesis Mission Project Agreement – AI Bridge Agreement
Each OT agreement will DOE will be comprised of:
1 Funding Agreement (Milestones or Reimbursement);
1 Appendix I (Miletone or Technical Objectives) to be negotiated;
1 Patent Rights Appendix II (401.14 or Simplified); and
1 Data Rights Appendix III (Open or Proprietary).
The application team will also be expected to negotiate a Project Agreement amongst their team,
which may include:
a Data Use Agreement; and/or
an AI Bridge agreement (for use with DOE/NNSA labs).
Editable Word versions of these documents will be shared during award negotiation.
Posted on Tuesday, March 31, 2026

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OTHER TRANSACTION AGREEMENT FOR ADVANCING THE GENESIS MISSION
BETWEEN
THE UNITED STATES DEPARTMENT OF ENERGY
AND
[AWARDEE ENTITY NAME]
The Genesis Mission is a historic national effort to catalyze new industries, create high-skill jobs,
and usher a new golden era of American discovery through artificial intelligence (AI) innovation.
Under the leadership of the United States Department of Energy (DOE or Agency) and the White
House Office of Science and Technology Policy, and in partnership with industry, academia, and
the National Laboratories, the Genesis Mission will build the American Science and Security
Platform to combine supercomputing, AI, next-generation quantum systems, and America’s most
advanced scientific instruments into a single, integrated engine of discovery that leverages the
singular scientific datasets and expertise of the DOE and National Nuclear Security
Administration (NNSA) Laboratory complex. The Genesis Mission is designed to ensure that
the United States leads in AI-enabled scientific discovery, energy innovation, and national
security capabilities.
This Genesis Mission Funding Agreement (“Agreement”) is made and entered into as of the
Effective Date by and between the United States Department of Energy (DOE), an agency of the
United States Government (Government), and [Awardee Entity Name] (Awardee)(collectively
the Parties), a [type of entity] organized and existing under the laws of [place of incorporation or
organization]. Further operational and technical details for the Awardee’s collaboration,
including with DOE/NNSA National Laboratories, will be governed by a Project Agreement,
including allocation of intellectual property between the collaborating parties.
NOW, THEREFORE, in consideration of the foregoing and for the mutual promises hereinafter
set forth, the Parties agree as follows:
Contents
Contents ........................................................................................................................................................ 1
SECTION I: Purpose and Scope of Work ..................................................................................................... 2
SECTION II: Authority to Enter into the Agreement ................................................................................... 3
SECTION III: Intellectual Property .............................................................................................................. 3
SECTION IV: Intellectual Property Warranty .............................................................................................. 3
SECTION V: Research, Technology and Economic Security (RTES) ......................................................... 3
SECTION VI: Reporting Requirements and Approvals ............................................................................... 6
SECTION VII: Modifications, Disputes, Termination, and Assignment ...................................................... 7
SECTION VIII: Miscellaneous ................................................................................................................... 10
SECTION IX: DEFINED TERMS ............................................................................................................. 14
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The following is intended to be understood using the definitions defined in Section IX.
SECTION I: Purpose and Scope of Work
The purpose of this Agreement is to establish a public-private partnership that advances the
Genesis Mission’s core objectives: to accelerate breakthroughs in energy dominance, discovery
science, and national security through the development and deployment of self-improving AI
models and integrated scientific workflows. By combining DOE/NNSA’s unique data assets and
research infrastructure with the agile development practices and specialized expertise of private
industry, this partnership will drive innovation at scale and speed, directly supporting the nation’s
strategic goals.
Through this Agreement, the Awardee will conduct research and development activities aligned
with the milestones set forth herein, contributing to the creation of the American Science and
Security Platform. The outcomes of this partnership are intended to double U.S. research and
development productivity within a decade, strengthen U.S. competitiveness, and ensure that
America leads in AI-enabled scientific discovery, energy innovation, and national security
capabilities.
This Agreement reflects the urgency and ambition of the Genesis Mission: to move with speed
and purpose, uniting government, industry, and academia in a coordinated national effort that
will fundamentally transform how science and engineering are conducted for the benefit of the
American people.
Authorization and funding for subsequent Milestones will be contingent upon DOE approval that
Milestone completion criteria have been met and the availability of funding to continue work
under this Agreement.
Article 1.1: Period of Performance
This Agreement governs the performance of the Milestones.
The Awardee shall commence performance of this Agreement in accordance with the Agreement
terms and conditions on the Effective Date and continue until the completion or upon expiration
of the deadline, i.e., “Quarter Due (from project start)” for all the Milestones, unless otherwise
agreed upon by the parties in writing. Additionally, DOE may terminate the agreement upon a
failure to achieve a milestone by such deadline. The completion date for this Agreement is
[TBD]. This date may be extended by written mutual agreement of the Parties.
Article 1.2: Financial Obligation
A. Obligation
DOE’s liability to make Milestone Payments is limited to only those funds detailed in this
Agreement and the availability of appropriated funding to continue work under this
Agreement.
B. Cost-Share
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The Awardee must provide any applicable “Awardee Cost Share” identified for each
Milestone. The cost share must:
• Be verifiable when the application is submitted.
• Be cash, cash equivalents, or in-kind contributions.
• Come from non-federal sources (unless otherwise allowed by law), such as project
participants, state or local governments, or other third-party financing.
Unless otherwise agreed upon before completion of Milestone 1, the Cost Share must meet
requirements set forth in 2 C.F.R. § 200.306 and 910.130, and the cost principles set forth
in 2 C.F.R. §§ 200.400-476 and 2 C.F.R. §§ 910.352.
SECTION II: Authority to Enter into the Agreement
Article 2.1: Authority
This is a research, development, and/or demonstration agreement entered into pursuant to DOE’s
OT Authorities, including 42 U.S.C.§ 7256(a), (g).1 This Agreement between DOE and Awardee
is a transaction other than a procurement, grant, cooperative agreement, or loan. Accordingly,
only those terms, conditions, provisions and requirements set forth in this Agreement and any
other terms, conditions, provisions and requirements prescribed by law and regulations for other
transaction agreements under DOE’s OT Authorities, including 42 U.S.C.§ 7256(a), (g),2 some
of which are expressly incorporated herein by reference, apply to this Agreement.
This Agreement is valid only if it is in writing and is signed, including electronically, by the AO
and by the Awardee’s authorized representative.
SECTION III: Intellectual Property
Intellectual Property Rights are government by the provisions of Appendix II – Patent Rights and
Appendix III – Data Rights.
SECTION IV: Intellectual Property Warranty
The Awardee represents and warrants that any data used in the performance of Milestones,
including but not limited to AI Artifacts, would not infringe upon any intellectual property right
of any third party, such as any patent, copyright, trade secret, or other intellectual property right
and that it has all necessary rights in said data for delivery as described in the Milestones.
Awardee agrees that it has exercised reasonable efforts and diligence in making this
representation and warranty. The foregoing representation and warranty shall be ongoing during
the term of the Agreement.
SECTION V: Research, Technology and Economic Security (RTES)
1 2 C.F.R. § 930.
2 2 C.F.R. § 930.
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Article 5.1: Foreign Entity Participation
A Foreign Entity is not eligible to participate as either an Awardee or Subawardee. In limited
circumstances DOE may approve a waiver to allow a foreign entity to participate.
The Awardee must work with the DOE Laboratory to ensure compliance with all applicable
safety, health, access to information, security and environmental regulations and the
requirements of the Department and the DOE Laboratory. In the event the Awardee fails to
comply with said regulations and requirements, the DOE Laboratory may, without prejudice to
any other legal or contractual rights, issue an order stopping all or any part of Awardee's
activities with the DOE Laboratory.
Article 5.2: Prohibition on Incorporation in or Ownership or Control by Foreign Countries
of Concern
Throughout the life of the Agreement, the Awardee, parent company, and project team members
shall not be solely incorporated in, or owned or controlled by, or subject to the direction of a
Foreign Country of Concern. If there is a change in ownership or control that increases foreign
ownership or control by Foreign Country of Concern or a change that effectively makes the
entity subject to the direction of a Foreign Country of Concern, the Awardee must immediately
alert the AO.
Article 5.3: Entity of Concern Prohibition
No Entity of Concern as defined in Section 10114 of Public Law 117-167 (42 USC 18912) may
participate in the performance of the Milestones.
Article 5.4: Malign Foreign Talent Recruitment Program Prohibition
Individuals participating in a Malign Foreign Talent Recruitment Program, as defined in Section
10638(4) of P.L. 117-167 (42 USC 19237(4), 19232), are prohibited from participating in in the
performance of the Milestones.
Article 5.5: Due Diligence Reviews and Disclosures
The Agreement is subject to a post-selection and ongoing research, technology, and economic
security risk review and monitoring to identify potential risks of undue foreign influence. As part
of the review, the Awardee must cooperate with DOE requests for information, including the
following required disclosures and certifications for all covered individuals listed on the
application and entities which must be updated within fifteen (15) business days of any changes
except advanced notice to DOE must be given for changes relating to a Foreign Country of
Concern: DOE Common Form for Current and Pending (Other) Support (42 USC 6605)
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including a Malign Foreign Talent Recruitment Program certification3, DOE Common Form for
Biosketch (NSPM-33), and Transparency of Foreign Connections Transparency of Foreign
Connections | Department of Energy (NSPM-33). New covered individuals listed on the
application and entities added during the term of the Agreement must provide the disclosures
above and receive DOE approval before participating. DOE may share information regarding the
risks identified as part of the RTES due diligence review process or monitoring with other
Federal agencies.
In the event an RTES risk is identified, or the required disclosures, certifications, or updates, are
not submitted, or there is non-compliance with any provision of this Section V, DOE may require
risk mitigation measures, including but not limited to, requiring that an individual or entity not
participate in the performance of this Agreement, or implementing other controls such as data
access restrictions, enhanced monitoring, or project scope adjustments, as determined necessary
by DOE to safeguard national security and economic interests. If significant risks are identified
and cannot be sufficiently mitigated, DOE may withhold or recapture a Milestone Payment or
terminate the OT Agreement.
3 All covered individuals must provide a separate disclosure statement listing the required
information above regarding current and pending support. Each Covered Individual must sign
and date their respective certification statement:
I, [Full Name and Title], understand that I have been designated as a covered
individual by the Federal funding agency.
I certify to the best of my knowledge and belief that the information contained in this
Current and Pending Support Disclosure Statement is true, complete, and accurate. I
understand that any false, fictitious, or fraudulent information, misrepresentations,
half-truths, or omissions of any material fact, may subject me to criminal, civil, or
administrative penalties for fraud, false statements, false claims, or otherwise. (18
U.S.C. §§ 1001 and 287, and 31 U.S.C. §§ 3729-3733 and 3801-3812). I further
understand and agree that (1) the statements and representations made herein are
material to DOE’s funding decision, and (2) I have a responsibility to update the
disclosures during the period of performance of the award should circumstances
change which impact the responses provided above.
I also certify that, at the time of submission, I am not a party in a malign foreign
talent recruitment program. I further understand should I take action to involve
myself with a Malign Foreign Talent Recruitment Program during the period of
performance of the award, I must notify the Awardee’s Authorized Agent immediately,
but no later than five business days of taking such action and immediately recuse
myself from all DOE awards.
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DOE’s decision regarding a due diligence review is not appealable.
Article 5.6: Performance of Work in the United States
All work in performance of the Milestones must be performed in the United States (i.e., the
Awardee must expend 100% of the total project cost in the United States), unless the Awardee
requests and receives advance written authorization from the AO to perform certain work outside
of the United States. This provision does not apply to the acquisition of materials or components
of the Awardee’s project unless otherwise instructed in writing by the AO.
Article 5.7: Conflicts of Interest
The Awardee shall adopt and maintain a conflict of interest policy4 under this Agreement, which
shall be provided to DOE upon request. This conflict of interest policy shall address both
individual conflicts of interest as related to Key Personnel and Organizational Conflicts of
Interest, including but not limited to financial conflicts of interest (FCOI) (i.e., managed and
unmanaged/unmanageable).
The Awardee shall disclose in writing to the AO any potential or actual Conflict of Interest as
soon as reasonably practical after discovery thereof. The Awardee and the DOE shall jointly
develop a mitigation plan to address Conflicts of Interest as they arise.
SECTION VI: Reporting Requirements and Approvals
Article 6.1: Milestones- Review, Acceptance or Rejection of Milestones
1. The Awardee shall be responsible for the on-time delivery and satisfactory completion of
all Milestones. Unless otherwise specified by the AO, in writing, in advance of the
deadline, all documents, including reports, are to be furnished to the DOE via email to the
AO.
2. DOE shall accept or reject delivered Milestones as promptly as practicable after delivery,
unless otherwise specified in the Agreement. DOE shall determine adequacy of each
deliverable promptly and will notify the Awardee in writing of the acceptance or rejection
of the deliverable within ten (10) business days. DOE’s failure to review and accept or
reject the work shall not relieve the Awardee from responsibilities under this Agreement,
nor create liability for the Government. Acceptance will not be unreasonably withheld or
delayed.
3. If DOE rejects the deliverable, the notice of rejection will provide the Awardee a specific
description of the identified nonconformity. The Awardee will have the opportunity to
cure under a timeline agreed to by the Awardee and the AO.
4. Payment for Milestones.
4 See https://www.energy.gov/management/department-energy-interim-conflict-interest-policy-
requirements-financial-assistance
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a. Milestone Payment may only be requested after DOE has notified the Awardee
of its acceptance of the Milestone deliverable and verified that the Milestone
completion criteria have been met. DOE will provide payment for the completed
Milestone within 30 calendar days of receipt of a Milestone payment request
submitted in accordance with the paragraphs below. Only the Awardee may
submit payment requests to DOE. Subawardees may not submit payment
requests directly to DOE.
b. Milestone Payment Request Submittal. The Awardee is required to submit
payment requests electronically through DOE’s Oak Ridge Financial Service
Center Vendor Inquiry Payment Electronic Reporting System (VIPERS). To
access and use VIPERS, they are required to enroll and login to the VIPERS
website (https://vipers.doe.gov/ or https://vipers.doe.gov/RequestAccess.aspx).
DOE will disburse payments under this Agreement through the Automated
Clearing House (ACH) VIPERS. The Awardee may check the status of its
payments at the VIPERS website. All payments are made by electronic funds
transfer to the bank account identified on the ACH Vendor/Miscellaneous
Payment Enrollment Form (SF 3881) filed by the Awardee.
c. Before any payment is submitted, the Awardee shall provide a disclosure of
each Subject Invention and a properly executed Invention Certification as
required by Article IV.
SECTION VII: Modifications, Disputes, Termination, and Assignment
Article 7.1: Modifications
Proposals for modifications will be documented in writing and submitted by the Awardee to the
AO, or by DOE to the Awardee. Parties may request the technical, schedule, and financial
impact of the proposed modification as appropriate.
Any modification, including changes to the Milestones, to this Agreement shall be executed in
writing and signed by an authorized representative of DOE and the Awardee. The DOE is not
obligated to pay for costs related to modifications. Further, the Awardee is not obligated to
accomplish work prior to mutual agreement between the AO and the Awardee and performs
work at risk of not being compensated without mutual agreement, in writing.
For minor or administrative modifications (e.g., changes to the paying office or appropriation
data), Awardee approval is not required, unless Awardee timely objects, in writing, to such a
proposed minor or administrative modification. In the event of any objection, the modification
must be bilaterally executed.
Article 7.2: Disputes
A. A party that believes there is a dispute about an issue under this Agreement will submit to
the other party, via email with receipt acknowledged by the other party, a summary of the
dispute. Disputes will be raised by the Awardee to the AO or by the AO to the Awardee
point of contact. The AO and awardee will work together to resolve issues or disputes,
including using any alternative dispute mechanisms to the maximum extent practicable.
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Informal resolution, including resolution through an alternative dispute resolution
mechanism, will be preferred over formal procedures, to the extent practicable.
B. DOE Final Determination. If a dispute is not resolved informally between the DOE AO
and the Awardee, DOE shall provide a written determination signed by the AO, setting
forth DOE’s final disposition of such dispute. The determination will include a summary
of the dispute and the factual, legal, and if appropriate, policy reasons for DOE’s
disposition of the dispute.
C. Right of Appeal:
a. The final determination under this Article may only be appealed to the cognizant
Senior Procurement Executive (SPE), as defined by 41 U.S.C. 1702(c) for the
following actions:
i. A DOE determination that the awardee has failed to comply with the
applicable requirements of the Agreement;
ii. termination of an award, in whole or in part, by DOE;
iii. the application of DOE of an indirect cost rate; and
iv. DOE disallowance of costs.
b. The appeal must be received by DOE within 90 days of receipt of the final
determination. The mailing address for the DOE SPE is 1000 Independence Ave.,
SW., Washington DC 20585.
c. In reviewing disputes authorized under this article, the SPE will be bound by the
applicable law, statutes, and rules, including the requirements of this Article, and
by the terms and conditions of this Agreement.
d. The decision of the SPE shall be the final decision of DOE.
Article 7.3: Termination
A. Noncompliance. In the event the Awardee failed to comply with the terms and conditions
of this Agreement, the AO shall give written notice to the Awardee to comply, including the
factual and legal basis for the determination of noncompliance, the corrective actions, and the
date by which they must be taken (not less than 30 days), and which of the actions authorized
under 2 CFR § 930.345(a) DOE may take if the Awardee does not achieve compliance within the
time specified in the written notice. Failure to comply may result in termination of this
Agreement;
B. The Parties may mutually agree to terminate in whole or in part this Agreement by
providing at least 30 days advance written notice to the other party, provided such notice is
preceded by consultation between the Awardee and DOE and a reasonable determination by
either party that this Agreement will not produce beneficial results commensurate with the
expenditure of resources. Awardee and DOE will negotiate the termination conditions, including
the effective date and, in the case of partial termination, the portion to be terminated. If either the
Awardee or the DOE determines in the case of partial termination that the reduced or modified
portion of the Agreement will not accomplish its intended purpose, the Agreement may be
terminated in its entirety;
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B. DOE may terminate the agreement in whole or in part if the Awardee materially fails to
comply with the articles or terms and conditions of an agreement, whether stated in a Federal
statute, regulation, assurance, application, plan, or the notice of award fails to comply with the
articles and requirements of the Agreement. DOE shall promptly provide Awardee with a Cure
Notice identifying the Material Breach. Within ten (10) days after receipt of the Cure Notice,
Awardee shall respond in writing to the Cure Notice and provide DOE with a Cure Plan;
C. DOE may terminate the agreement if the Awardee files a bankruptcy petition that is not
dismissed within ten (10) business days, the Awardee is adjudicated bankrupt or is otherwise
insolvent, or the Awardee ceases to do business or otherwise terminates its business operations;
and
D. DOE may terminate the agreement if the Awardee fails to achieve a Milestone within the
time period specified in the Milestones.
E. Either party may terminate without cause upon thirty (30) days written notice to the other
party. If DOE terminates without cause, the Awardee shall submit an invoice to DOE based on
the prorated fixed price for any remaining Milestone and such proration will be based on effort
expended from the last Milestone payment up to the point of termination. The AO, in their
discretion, will determine if and how much of an appropriate prorated payment is warranted.
Selectee acknowledges and agrees that no other compensation, of any nature or type, shall be
payable hereunder following the termination of this Agreement.
F. In the event of termination, DOE and the Awardee shall meet to affect an orderly
termination of any ongoing or planned activities and will negotiate in good faith for the
disposition of the Awardee’s Limited Rights Data in the possession of DOE, if applicable. In no
case will the Government be liable for expenses or other financial obligations of the Awardee
incurred due to early termination.
G. In the event this Agreement is terminated for any reason under paragraphs A-D of this
Article, DOE has the right to unilaterally not pay for funding not yet disbursed pursuant to the
Milestones in Appendix I. Mutual termination does not impose any requirement that DOE pay in
whole or part for milestones not achieved.
H. In the event of any termination by DOE, neither DOE nor the Awardee shall be liable for
any loss of profits, revenue, or any indirect or consequential damages incurred by the other Party,
its contractors, subcontractors, or customers as a result of any termination of this Agreement.
DOE’s or the Awardee’s liability for any damages under this Agreement is limited solely to direct
damages, incurred by the other Party, as a result of any termination of this Agreement subject to
mitigation of such damages by the complaining party. However, in no instance shall DOE's
liability for termination exceed the total amount due under the milestone towards which the
Awardee is currently working under this Agreement.
Article 7.4 Assignment
Neither this Agreement nor any interest arising under it will be assigned by the Awardee or DOE
without the express written consent of the other party.
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SECTION VIII: Miscellaneous
Article 8.1: Export Control
Awardee is responsible for ensuring compliance with all applicable United States Export Control
laws and regulations relating to any work performed under a resulting Agreement. The Awardee
must immediately report to DOE any export control violations related to this Agreement and
provide the corrective action(s) to prevent future violations.
Article 8.2: Communication
A. Administration. Unless otherwise provided in this Agreement, approvals permitted or
required to be made by DOE, or any other document that binds the government, may be
made only by the DOE Agreements Officer who has a warrant including the award of
Other Transaction Agreements. Administrative and contractual matters under this
Agreement shall be referred to the following representatives of the Parties:
DOE Agreements Officer: XXXX
DOE Grants and Agreements Specialist: XXXX
DOE Patent Counsel: Michael Dobbs, (331) 465-1317, mike.dobbs@science.doe.gov (for
all questions regarding Intellectual Property matters)
Awardee: XXXX
B. Milestone Deliverables. Milestone deliverable matters under this Agreement shall be
referred to the following representative:
DOE Program Manager: XXXX
Awardee: XXXX
C. Technical. Technical matters under this Agreement shall be referred to the following
representatives:
DOE Program Manager: XXXX
Awardee: XXXX
D. Change of Designated Representative. Each party may change its representatives named
in this Article by written notification to the other party.
Article 8.3: Closeout
Upon Agreement completion, the AO must close out the Agreement in accordance with the
Federal-Wide Research Terms and Conditions and DOE Standard Research Terms and
Conditions Agency Specific Requirements, Financial Assistance Handbook Chapter 30A -
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Closeout of Financial Assistance Instruments, 2 CFR § 930, and the DOE Office of Scientific
and Technical Information (OSTI) E-Link website (https://www.osti.gov/elink/), including
ensuring all deliverables have been accepted and any audits completed. The Awardee agrees to
provide DOE all relevant documents requested to closeout this Agreement.
Article 8.4: Resolution of Conflicting Conditions
Any apparent inconsistency between Federal statutes and regulations and the terms and
conditions contained in this Agreement must be referred to the AO for guidance.
Article 8.5: Suspension and Debarment
The Awardee shall not contract with entities that are currently listed on the System for Award
Management as suspended or debarred in support of this Agreement. In accordance with
Executive Orders 12549 and 12689, the regulations at 2 CFR part 180, Guidance for
Governmentwide Debarment and Suspension (Non procurement) are applicable to this
Agreement.
Article 8.6: Inspector General
A. The Awardee is required to provide any information, documents, site access, or other
assistance requested by DOE or Federal auditing agencies (e.g., DOE Inspector General,
Government Accountability Office, Defense Contracting Audit Agency, United States Digital
Service, Department of Government Efficiency) for work related to this Agreement. Single
Audits are not required under this Agreement.
B. In the event that the Awardee has Federal funding from another Federal agency or entity, in
accordance with 42 U.S.C. § 7137, the Comptroller General of the United States, or any of
his duly authorized representatives, shall have access to and the right to examine any books,
documents, papers, records, or other recorded information of the Awardee of Federal funds or
assistance under this Agreement, including contracts between the Awardee and other entities.
Article 8.7: System for Award Management
The Awardee is required to be registered in the System for Award Management (SAM,
www.sam.gov). The Awardee agrees to opt in to SAM notifications and monitor SAM for DOE
issued information.
Article 8.8: Insolvency, Bankruptcy or Receivership
A. The Awardee shall immediately notify DOE of the plans for, or significant likelihood of, any
of the following events: (i) the Awardee or the Awardee’s parent’s filing of a voluntary case
seeking liquidation or reorganization under the Bankruptcy Act; (ii) the Awardee’s consent to
the institution of an involuntary case under the Bankruptcy Act against it or its parent; (iii)
the filing of any similar proceeding for or against the Awardee or the Awardee’s parent, or its
consent to, the dissolution, winding-up or readjustment of its debts, appointment of a
receiver, conservator, trustee, or other officer with similar powers over it, under any other
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applicable state or federal law; or (iv) the Awardee’s insolvency due to its inability to pay its
debts generally as they become due.
B. Such notification shall be in writing and shall: (i) specifically set out the details of the
occurrence of an event referenced in paragraph A; (ii) provide the facts surrounding that
event; and (iii) provide the impact such event will have on the project being funded by this
Agreement.
C. Upon the occurrence of any of the four events described in the first paragraph, DOE reserves
the right to conduct a review of the Awardee’s agreement to determine their compliance with
the required elements of the agreement. If the DOE review determines that there are
significant deficiencies or concerns with the Awardee’s performance under the agreement,
DOE reserves the right to impose additional requirements, as needed.
D. Failure of the Awardee to comply with this term may be considered a Material Breach of this
Agreement.
Article 8.9: Fraud, Waste and Abuse
The Awardee shall disclose, in a timely manner, in writing to DOE all violations of Federal
criminal law involving fraud, bribery, or gratuity violations by Awardee, its employees, and
agents that potentially affect the agreement. Compliance with the Rules of Professional Conduct
does not violate this paragraph. The DOE Office of Inspector General maintains a Hotline for
reporting allegations of fraud, waste, abuse, or mismanagement. To report such allegations,
please visit https://www.energy.gov/ig/ig-hotline.
Article 8.10: Choice of Law
The Awardee acknowledges that this Agreement is subject to federal law of the United States.
Article 8.11 NONDISCLOSURE AND CONFIDENTIALITY AGREEMENTS
ASSURANCES
A. By entering into this Agreement, the Awardee does not and will not require its employees
or contractors to sign internal nondisclosure or confidentiality agreements or statements
prohibiting or otherwise restricting its employees or contactors from lawfully reporting
waste, fraud, or abuse to a designated investigative or law enforcement representative of a
federal department or agency authorized to receive such information.
B. The undersigned further attests that Awardee will not implement or enforce any
nondisclosure and/or confidentiality policy, form, or agreement it uses in the performance
of the Milestones unless it contains the following provisions:
(1) These provisions are consistent with and do not supersede, conflict with, or
otherwise alter the employee obligations, rights, or liabilities created by existing
statute or Executive order relating to (a) classified information, (b) communications
to Congress, (c) the reporting to an Inspector General or the Office of Special
Counsel of a violation of any law, rule, or regulation, or mismanagement, a gross
waste of funds, an abuse of authority, or a substantial and specific danger to public
health or safety, or (d) any other whistleblower protection. The definitions,
requirements, obligations, rights, sanctions, and liabilities created by controlling
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Executive orders and statutory provisions are incorporated into this agreement and
are controlling.
(2) The limitation above shall not contravene requirements applicable to Standard Form
312, Form 4414, or any other form issued by a Federal department or agency
governing the nondisclosure of classified information.
C. Notwithstanding provision listed in paragraph (A), a nondisclosure or confidentiality
policy form or agreement that is to be executed by a person connected with the conduct of
an intelligence or intelligence-related activity, other than an employee or officer of the
United States Government, may contain provisions appropriate to the activity for which
such document is to be used. Such form or agreement shall, at a minimum, require that the
person will not disclose any classified information received during such activity unless
specifically authorized to do so by the United States Government. Such nondisclosure or
confidentiality forms shall also make it clear that they do not bar disclosures to Congress,
or to an authorized official of an executive agency or the Department of Justice, that are
essential to reporting a substantial violation of law.
Article 8.12 CORPORATE FELONY CONVICTION AND FEDERAL TAX LIABILITY
ASSURANCES
By entering into this Agreement, the undersigned attests that the Awardee has not been
convicted of a felony criminal violation under Federal law in the 24 months preceding the
date of signature.
The undersigned further attests that Awardee does not have any unpaid Federal tax liability
that has been assessed, for which all judicial and administrative remedies have been exhausted
or have lapsed, and that is not being paid in a timely manner pursuant to an agreement with
the authority responsible for collecting the tax liability.
For purposes of these assurances, the following definitions apply: A Corporation includes any
entity that has filed articles of incorporation in any of the 50 states, the District of Columbia,
or the various territories of the United States [but not foreign corporations]. It includes both
for- profit and non-profit organizations.
Article 8.13 LOBBYING RESTRICTIONS
The Awardee agrees that none of the funds under this Agreement shall be expended, directly
or indirectly, to influence congressional action on any legislation or appropriation matters
pending before Congress, other than to communicate to Members of Congress as described in
18 U.S.C. § 1913. This restriction is in addition to those prescribed elsewhere in statute and
regulation.
Article 8.14 INDEMNIFICATION
The Awardee will indemnify the Government and its officers, agents, or employees for any
and all liability, including litigation expenses and attorneys' fees, arising from suits, actions,
or claims of any character for death, bodily injury, or loss of or damage to property or to the
environment, resulting from the project, except to the extent that such liability results from
the direct fault or negligence of Government officers, agents or employees, or to the extent
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such liability may be covered by applicable allowable costs provisions.
Article 8.15 STATEMENT OF FEDERAL STEWARDSHIP
DOE will exercise normal Federal stewardship in overseeing the project activities performed
under this Agreement. Stewardship activities include, but are not limited to, conducting site
visits; reviewing performance and financial reports; providing technical assistance and/or
temporary intervention in unusual circumstances to correct deficiencies which develop during
the project; assuring compliance with terms and conditions; and reviewing technical performance
after project completion to ensure that the Technical Objectives have been accomplished.
Article 8.16 SITE VISITS
DOE authorized representatives have the right to make site visits at reasonable times to review
project accomplishments and management control systems and to provide technical assistance, if
required. You must provide, and must require your subrecipients to provide, reasonable access to
facilities, office space, resources, and assistance for the safety and convenience of the
government representatives in the performance of their duties. All site visits and evaluations
must be performed in a manner that does not unduly interfere with or delay the work.
SECTION IX: DEFINED TERMS
In addition to terms defined in other provisions of this Agreement, the terms and phrases as
defined below, when used herein as the defined term shall have the following meanings:
“AO” shall mean DOE Agreements Officer.
“AI Artifact” shall mean any dataset, code, scripts, pipelines, models (including architectures,
parameters, and weights), prompts, outputs, documentation, metadata, test/evaluation suites,
synthetic data, and system/model/data cards created, used, or improved under the Project
Agreement.
“Covered Individual” means an individual who (a) contributes in a substantive, meaningful way
to the scientific development or execution of the scope of work of the agreement, and (b) is
designated as a covered individual by DOE. DOE designates as covered individuals any principal
investigator (PI); project director (PD); co-principal investigator (Co-PI); co-project director
(Co-PD); project manager; and any individual regardless of title that is functionally performing
as a PI, PD, Co-PI, Co-PD, or project manager. Submission of a current and pending support
disclosure and/or biosketch/resume for a particular person serves as an acknowledgement that
DOE designates that person as a covered individual. DOE may further designate covered
individuals during the agreement period of performance.
“Curated Data” shall mean any Data generated in Milestone 2.
“Data” shall mean recorded information, regardless of form or the media on which it may be
recorded, including Technical Data. The term does not include information incidental to Other
Transaction (OT) Agreement administration, such as financial, administrative, cost or pricing or
management information.
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“Data and Intellectual Property Rights Certification” shall mean a formal written statement,
signed by an authorized representative of the Awardee, delivered as part of a Milestone, which
attests that the Awardee has the necessary legal and contractual rights to all data, software, and
intellectual property used in a delivered AI Artifact, sufficient to enable its deployment and use
as contemplated under this Agreement.
“Effective Date” shall mean the date upon which the Agreement will enter into force which will
be the date of the last signatory to the Agreement.
“Foreign Country of Concern” shall mean the People's Republic of China, the Russian
Federation, the Democratic People's Republic of Korea, the Islamic Republic of Iran, and
Belarus, or any other country determined to be a foreign country of concern by the Secretary of
State, and including countries of risk designated by DOE. This list is subject to change. DOE
reserves the right, in the exercise of its absolute discretion, to add or subtract any foreign country
determined to be of risk consistent with DOE policies by a unilateral amendment to this
Agreement.
“Foreign Entity” shall mean any corporation, business association, partnership, trust, society, or
any other entity or group that is not incorporated or organized to do business in the United States,
as well as international organizations, foreign governments and any agency or subdivision of
foreign governments including state-owned enterprises. The term “Foreign Countries of
Concern” and “Foreign National” are separate terms and not necessarily covered under the
definition of a Foreign Entity although “Foreign Countries of Concern” are also “Foreign
Entities”.
“Foreign National” shall mean any individual other than a U.S. citizen.
“Government” shall mean the Federal government of the United States of America including its
agencies and departments.
“Intellectual Property” shall mean any and all subject inventions, copyrights, and trademarks
developed by an Awardee.
"Key Personnel” shall mean the voting board members, C-suite executives, and other senior
personnel identified by Awardee.
“Limited rights data” shall mean data developed at private expense and first produced outside
the performance of this agreement that embody trade secrets, or are commercial, or financial, and
confidential or privileged.
“Material Breach” shall mean a breach by the Awardee or DOE of any of its obligations under
this Agreement including those which result, or are likely to result, in the inability of the
Awardee or DOE to effectively complete the Milestones. This includes but is not limited to the
failure to achieve Milestones, breach of provisions included in the Agreement.
“Milestone” shall mean a deliverable identified in Appendix I - Milestones, Reporting,
Deliverables.
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“Milestones” shall mean the collective deliverables identified in Appendix I - Milestones,
Reporting, Deliverables.
“Milestone Payment” shall mean DOE’s payment to the Awardee for achievement of a
Milestone.
“Model Data” shall mean any data generated in the performance of Milestone 3.
“Non-Public Data” shall mean Data that has not been publicly distributed to others without
restriction on further dissemination. Specifically excluded from this definition is data shared at a
conference, public talk or other public forum if the data shared is shared without restriction on
further dissemination.
“Organizational Conflicts of Interest” shall mean where the Awardee is unable, or appears to be
unable, to be impartial in conducting activities under this agreement due to its relationships with
affiliates, organizations, or any other interested party.
“Project Agreement” means an agreement between the Awardee and their partners/collaborators
in the performance of the Technical Objectives, which is intended to address all data rights,
including allocation of rights to Project AI Artifacts. The Project Agreement includes
agreements with DOE National Laboratory, preferably using the preapproved AI Bridge
Agreement containing a Data Use Agreement (DUA) template that complies with Article 3.2.
“Project AI Artifact” shall mean AI Artifacts developed in performance of the Milestones,
including a Project Agreement.
“Unlimited Rights” shall mean the right of the Government to use, disclose, reproduce, prepare
derivative works, distribute copies to the public, and perform publicly and display publicly, in
any manner and for any purpose, and to have or permit others to do so.
IN WITNESS WHEREOF, the Parties have executed this Agreement.
U.S. DEPARTMENT OF ENERGY AWARDEE
By:_____________________________ By: ______________________________
[NAME] [NAME]
Agreements Officer TITLE
Dated: _______________________________ Dated: _______________________________
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APPENDIX I
Milestones, Reporting, Deliverables
Mile- Milestone Milestone Description (including Data Protection Quarter Estimated Federal Awardee Cost
stone # Title Completion Criteria) Due Total payment upon Share($)
(from Cost ($) 5 completion ($)
project
start)
1 Agreements The execution of all agreements All data delivered to 1 $TBD $TBD
and within the Application Team within DOE must be
Alignment 90 days of award. delivered with
1. DOE must be provided with a unlimited rights.
copy of the executed Project
Agreement showing The Awardee may
execution by at least the redact business
Application Team and sensitive portions of
meeting all requirements of the Project
the award, including Agreement that are
requirements listed in Article not relevant to
3.2(1)(d). The Team is confirming
encouraged to leverage execution of the
templates provided by DOE, agreement amongst
especially when working the party and
with a DOE Laboratory. compliance with the
award terms.
5 All contributions estimated to achieve milestones, including in kind contribution.
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Mile- Milestone Milestone Description (including Data Protection Quarter Estimated Federal Awardee Cost
stone # Title Completion Criteria) Due Total payment upon Share($)
(from Cost ($) 5 completion ($)
project
start)
2 Data The curation of all scientific data All data delivered to XX
Curation within the Application Team so that DOE may be
the data is structured, cleaned, and identified as
preprocessed in a way that makes it Protected Data
suitable for use in artificial except:
intelligence and machine learning 1.a summary report;
models no later than XXX. and
1. The curated data must 2.(a) general test
include [Insert a Specific or performance
description of the data to be results
curated] demonstrating
2. The curated data will be technical
delivered to [Insert Where breakthroughs,
Curated Data must be milestones or
delivered. This should achievements; (b)
typically include delivery to general data
the American Science Cloud] demonstrating
with the following rights: progress toward
[Insert Specific Data rights meeting DOE's
approved by cognizant DOE technical targets and
Patent Counsel] (c) any research data
3. The curated data must: contained in
[Insert specific requirements] publications
[including adherence to data resulting from the
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Mile- Milestone Milestone Description (including Data Protection Quarter Estimated Federal Awardee Cost
stone # Title Completion Criteria) Due Total payment upon Share($)
(from Cost ($) 5 completion ($)
project
start)
quality standards as may be work under the
established by the National agreement.
Science and Technology
Council (NSTC) Machine
Learning and AI
Subcommittee or other
relevant Federal guidance, to
ensure suitability for artificial
intelligence and machine
learning models, and
specifically for use in
developing next-generation
microelectronics and
accelerating innovation in
discovery science and
engineering for new energy
technologies via the
American science cloud,
while maintaining
appropriate privacy and
confidentiality protections.]
4. As a condition of Milestone
completion, the Awardee
shall deliver to the
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Mile- Milestone Milestone Description (including Data Protection Quarter Estimated Federal Awardee Cost
stone # Title Completion Criteria) Due Total payment upon Share($)
(from Cost ($) 5 completion ($)
project
start)
Agreements Officer (AO) a
signed Data and Intellectual
Property Rights Certification
for the delivered model and
all its constituent
components. This
certification shall, at a
minimum, attest to the
following:
a.That the Awardee has conducted a
thorough review of all datasets,
software libraries, pre-trained
models, and any other intellectual
property (collectively, “Model
Components’) used in the creation,
training, and operation of the
delivered AI model.
b.That the Awardee possesses
sufficient rights to all Model
Components to permit the
deployment of the model on the
American Science Cloud, consistent
with the terms of this Agreement, the
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Mile- Milestone Milestone Description (including Data Protection Quarter Estimated Federal Awardee Cost
stone # Title Completion Criteria) Due Total payment upon Share($)
(from Cost ($) 5 completion ($)
project
start)
associated Project Agreement, and
the requirements of Public Law 119-
21, as applicable.
c.That the deployment and use of
the model as described in this
Agreement does not infringe upon
any third-party patent, copyright,
trade secret, or other intellectual
property right.
5. d. That all data sourcing and
licensing documentation has
been archived and is
available for inspection by
DOE upon request.
3 Creation of Creation of artificial intelligence All data delivered to
an Artificial models no later than XXX. DOE may be
Intelligence 1. The delivered model must identified as
Model have been created using the Protected Data
data curated in milestone except:
two. 1.a summary of the
2. The model must perform the report; and
following functionality: 2.(a) general test
[INSERT SPECIFIC or performance
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Mile- Milestone Milestone Description (including Data Protection Quarter Estimated Federal Awardee Cost
stone # Title Completion Criteria) Due Total payment upon Share($)
(from Cost ($) 5 completion ($)
project
start)
DETAILS ON THE results
PERFORMANCE demonstrating
CHARACTERISTICS OF A technical
SUCCESSFUL MODEL] breakthroughs,
[and demonstrate capabilities milestones or
for interpretability, control, achievements; (b)
and robustness against general data
adversarial attacks, as well as demonstrating
incorporating secure-by- progress toward
design principles to mitigate meeting DOE's
risks of misuse or technical targets and
unauthorized access. (c) any research data
Furthermore, the models contained in
shall be designed for publications
seamless integration with and resulting from the
provision to the scientific work under the
community through the agreement.
American science cloud,
adhering to its operational
and data sharing standards to
accelerate innovation in
discovery science and
engineering for new energy
technologies.]
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Mile- Milestone Milestone Description (including Data Protection Quarter Estimated Federal Awardee Cost
stone # Title Completion Criteria) Due Total payment upon Share($)
(from Cost ($) 5 completion ($)
project
start)
3. The model will be:
a. provided to the
scientific community
through the American
science cloud to
accelerate innovation
in discovery science
and engineering for
new energy
technologies; and
b. deployed to accelerate
innovation in
discovery science and
engineering for new
energy technologies
by: [DESCRIBE
HOW THE MODEL
WILL BE
DEPLOYED
CONSISTENT WITH
P.L. 119-21, §50404]
4. As a condition of Milestone
completion, the Awardee shall
deliver to the Agreements
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Mile- Milestone Milestone Description (including Data Protection Quarter Estimated Federal Awardee Cost
stone # Title Completion Criteria) Due Total payment upon Share($)
(from Cost ($) 5 completion ($)
project
start)
Officer (AO) a signed Data and
Intellectual Property Rights
Certification for the delivered
model and all its constituent
components. This certification
shall, at a minimum, attest to the
following:
a.That the Awardee has conducted a
thorough review of all datasets,
software libraries, pre-trained
models, and any other intellectual
property (collectively, “Model
Components’) used in the creation,
training, and operation of the
delivered AI model.
b.That the Awardee possesses
sufficient rights to all Model
Components to permit the
deployment of the model on the
American Science Cloud, consistent
with the terms of this Agreement, the
associated Project Agreement, and
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Mile- Milestone Milestone Description (including Data Protection Quarter Estimated Federal Awardee Cost
stone # Title Completion Criteria) Due Total payment upon Share($)
(from Cost ($) 5 completion ($)
project
start)
the requirements of Public Law 119-
21, as applicable.
c.That the deployment and use of
the model as described in this
Agreement does not infringe upon
any third-party patent, copyright,
trade secret, or other intellectual
property right.
d. That all data sourcing and
licensing documentation has been
archived and is available for
inspection by DOE upon request.
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OTHER TRANSACTION AGREEMENT FOR ADVANCING THE GENESIS MISSION
BETWEEN
THE UNITED STATES DEPARTMENT OF ENERGY
AND
[AWARDEE ENTITY NAME]
The Genesis Mission is a historic national effort to catalyze new industries, create high-skill jobs,
and usher a new golden era of American discovery through artificial intelligence (AI) innovation.
Under the leadership of the United States Department of Energy (DOE or Agency) and the White
House Office of Science and Technology Policy, and in partnership with industry, academia, and
the National Laboratories, the Genesis Mission will build the American Science and Security
Platform to combine supercomputing, AI, next-generation quantum systems, and America’s most
advanced scientific instruments into a single, integrated engine of discovery that leverages the
singular scientific datasets and expertise of the DOE and National Nuclear Security
Administration (NNSA) Laboratory complex. The Genesis Mission is designed to ensure that
the United States leads in AI-enabled scientific discovery, energy innovation, and national
security capabilities.
This Genesis Mission Funding Agreement (“Agreement”) is made and entered into as of the
Effective Date by and between the United States Department of Energy (DOE), an agency of the
United States Government (Government), and [Awardee Entity Name] (Awardee)(collectively
the Parties), a [type of entity] organized and existing under the laws of [place of incorporation or
organization]. Further operational and technical details for the Awardee’s collaboration,
including with DOE/NNSA National Laboratories, will be governed by a Project Agreement,
including allocation of intellectual property between the collaborating parties.
NOW, THEREFORE, in consideration of the foregoing and for the mutual promises hereinafter
set forth, the Parties agree as follows:
Contents
SECTION I: Purpose and Scope of Work ..................................................................................................... 2
SECTION II: Authority to Enter into the Agreement ................................................................................... 4
SECTION III: Intellectual Property .............................................................................................................. 4
SECTION IV: Intellectual Property Warranty .............................................................................................. 4
SECTION V: Research, Technology and Economic Security (RTES) ......................................................... 5
SECTION VI: Reporting Requirements and Approvals ............................................................................... 7
SECTION VII: Modifications, Disputes, Termination, and Assignment ...................................................... 7
SECTION VIII: Miscellaneous ................................................................................................................... 10
SECTION IX: DEFINED TERMS ............................................................................................................. 14
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The following is intended to be understood using the definitions defined in Section IX.
SECTION I: Purpose and Scope of Work
The purpose of this Agreement is to establish a public-private partnership that advances the
Genesis Mission’s core objectives: to accelerate breakthroughs in energy dominance, discovery
science, and national security through the development and deployment of self-improving AI
models and integrated scientific workflows. By combining DOE/NNSA’s unique data assets and
research infrastructure with the agile development practices and specialized expertise of private
industry, this partnership will drive innovation at scale and speed, directly supporting the nation’s
strategic goals.
Through this Agreement, the Awardee will conduct research and development activities aligned
with the milestones set forth herein, contributing to the creation of the American Science and
Security Platform. The outcomes of this partnership are intended to double U.S. research and
development productivity within a decade, strengthen U.S. competitiveness, and ensure that
America leads in AI-enabled scientific discovery, energy innovation, and national security
capabilities.
This Agreement reflects the urgency and ambition of the Genesis Mission: to move with speed
and purpose, uniting government, industry, and academia in a coordinated national effort that
will fundamentally transform how science and engineering are conducted for the benefit of the
American people.
Authorization and funding for subsequent Milestones will be contingent upon DOE approval that
Milestone completion criteria have been met and the availability of funding to continue work
under this Agreement.
Article 1.1: Period of Performance
This Agreement governs the performance of the Budget.
The Awardee shall commence performance of this Agreement in accordance with the Agreement
terms and conditions on the Effective Date and continue until the completion of the Budget. The
completion date for this Agreement is [TBD] though this date may be extended by written
mutual agreement of the Parties.
Article 1.2: Financial Obligation
1. Obligation
(a) DOE’s liability to make Payments is limited to only those funds detailed in the Technical
Objectives of this Agreement and the availability of appropriated funding to continue
work under this Agreement.
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(b) Cost Share
The Awardee must provide any applicable “Awardee Cost Share” identified for each
Technical Objective. This Cost share must
• Be verifiable when the application is submitted.
• Be cash, cash equivalents, or in-kind contributions.
• Come from non-federal sources (unless otherwise allowed by law), such as project
participants, state or local governments, or other third-party financing.
Unless otherwise agreed upon before award, the Cost Share must meet requirements set
forth in 2 C.F.R. § 200.306 and 910.130, and the cost principles set forth in 2 C.F.R. §§
200.400-476 and 2 C.F.R. §§ 910.352.
Article 1.3 PAYMENT PROCEDURES - ADVANCES THROUGH THE AUTOMATED STANDARD
APPLICATION FOR PAYMENTS (ASAP) SYSTEM
a. Method of Payment. Payment will be made by advances through the Department of
Treasury's ASAP system.
b. Requesting Advances. Requests for advances must be made through the ASAP system.
You may submit requests as frequently as required to meet your needs to disburse funds for
the Federal share of project costs. If feasible, you should time each request so that you
receive payment on the same day that you disburse funds for direct project costs and the
proportionate share of any allowable indirect costs. If same-day transfers are not feasible,
advance payments must be as close as is administratively feasible to actual disbursements.
c. Adjusting payment requests for available cash. You must disburse any funds that are
available from repayments to, and interest earned on, a revolving fund, program income,
rebates, refunds, contract settlements, audit recoveries, credits, discounts, and interest earned
on any of those funds before requesting additional cash payments from DOE.
d. Payments. All payments are made by electronic funds transfer to the bank account
identified on the ASAP Bank Information Form that you filed with the U.S. Department of
Treasury.
Article 1.4 REBUDGETING AND RECOVERY OF INDIRECT COSTS - REIMBURSABLE INDIRECT COSTS AND
FRINGE BENEFITS
a. If actual allowable indirect costs are less than those budgeted and funded under the
Agreement, you may use the difference to pay additional allowable direct costs during the
project period. If at the completion of the Agreement the Government's share of total
allowable costs (i.e., direct and indirect), is less than the total costs reimbursed, you must
refund the difference.
b. Recipients are expected to manage their indirect costs. DOE will not amend an Agreement
solely to provide additional funds for changes in indirect cost rates. DOE recognizes that the
inability to obtain full reimbursement for indirect costs means the recipient must absorb the
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underrecovery. Such underrecovery may be allocated as part of the organization's required
cost sharing.
Article 1.5 APPROVAL OF BUDGET PERIODS – FULLY FUNDED AWARDS (FEBRUARY 2015)
In the event that the Recipient does not submit a continuation application for subsequent Budget
Periods or DOE disapproves a continuation application for subsequent Budget Periods, the
maximum DOE liability to the Recipient is the total amount of funds available for the approved
Budget Period(s). In such an event, DOE reserves the right to not pay out any remaining funds.
SECTION II: Authority to Enter into the Agreement
Article 2.1: Authority
This is a research, development and/or demonstration agreement entered into pursuant to DOE’s
OT Authorities, including 42 U.S.C.§ 7256(a), (g)1. This Agreement between DOE and Awardee
is a transaction other than a procurement, grant, cooperative agreement, or loan. Accordingly,
only those terms, conditions, provisions and requirements set forth in this Agreement and any
other terms, conditions, provisions and requirements prescribed by law and regulations for other
transaction agreements under DOE’s OT Authorities, , including 42 U.S.C.§ 7256(a), (g)2, some
of which are expressly incorporated herein by reference, apply to this Agreement.
This Agreement is valid only if it is in writing and is signed, including electronically, by the AO
and by the Awardee’s authorized representative.
SECTION III: Intellectual Property
Intellectual Property Rights are government by the provisions of Appendix II – Patent Rights and
Appendix III – Data Rights.
SECTION IV: Intellectual Property Warranty
The Awardee represents and warrants that any data used in the performance of Milestones,
including but not limited to AI Artifacts, would not infringe upon any intellectual property right
of any third party, such as any patent, copyright, trade secret, or other intellectual property right
and that it has all necessary rights in said data for delivery as described in the Milestones.
Awardee agrees that it has exercised reasonable efforts and diligence in making this
representation and warranty. The foregoing representation and warranty shall be ongoing during
the term of the Agreement.
1 2 C.F.R. § 930
2 2 C.F.R. § 930
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SECTION V: Research, Technology and Economic Security (RTES)
Article 5.1: Foreign Entity Participation
A Foreign Entity is not eligible to participate as either an Awardee or Subawardee. In limited
circumstances DOE may approve a waiver to allow a foreign entity to participate.
The Awardee must work with the DOE Laboratory to ensure compliance with all applicable
safety, health, access to information, security and environmental regulations and the
requirements of the Department and the DOE Laboratory. In the event the Awardee fails to
comply with said regulations and requirements, the DOE Laboratory may, without prejudice to
any other legal or contractual rights, issue an order stopping all or any part of Awardee's
activities with the DOE Laboratory.
Article 5.2: Prohibition on Incorporation in or Ownership or Control by Foreign Countries
of Concern
Throughout the life of the Agreement, the Awardee, parent company, and project team members
shall not be solely incorporated in, or owned or controlled by, or subject to the direction of a
Foreign Country of Concern. If there is a change in ownership or control that increases foreign
ownership or control by Foreign Country of Concern or a change that effectively makes the
entity subject to the direction of a Foreign Country of Concern, the Awardee must immediately
alert the AO.
Article 5.3: Entity of Concern Prohibition
No Entity of Concern as defined in Section 10114 of Public Law 117-167 (42 USC 18912) may
participate in the performance of the Technical Objectives.
Article 5.4: Malign Foreign Talent Recruitment Program Prohibition
Individuals participating in a Malign Foreign Talent Recruitment Program, as defined in Section
10638(4) of P.L. 117-167 (42 USC 19237(4), 19232), are prohibited from participating in the
performance of the Technical Objectives.
Article 5.5: Due Diligence Reviews and Disclosures
The Agreement is subject to a post-selection and ongoing research, technology, and economic
security risk review and monitoring to identify potential risks of undue foreign influence. As part
of the review, the Awardee must cooperate with DOE requests for information, including the
following required disclosures and certifications for all covered individuals listed on the
application and entities which must be updated within fifteen (15) business days of any changes
except advanced notice to DOE must be given for changes relating to a Foreign Country of
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Concern: DOE Common Form for Current and Pending (Other) Support (42 USC 6605)
including a Malign Foreign Talent Recruitment Program certification3, DOE Common Form for
Biosketch (NSPM-33), and Transparency of Foreign Connections Transparency of Foreign
Connections | Department of Energy (NSPM-33). New covered individuals listed on the
application and entities added during the term of the Agreement must provide the disclosures
above and receive DOE approval before participating. DOE may share information regarding the
risks identified as part of the RTES due diligence review process or monitoring with other
Federal agencies.
In the event an RTES risk is identified, or the required disclosures, certifications, or updates, are
not submitted, or there is non-compliance with any provision of this Section V, DOE may require
risk mitigation measures, including but not limited to, requiring that an individual or entity not
participate in the performance of this Agreement, or implementing other controls such as data
access restrictions, enhanced monitoring, or project scope adjustments, as determined necessary
by DOE to safeguard national security and economic interests. If significant risks are identified
3 All covered individuals must provide a separate disclosure statement listing the required information
above regarding current and pending support. Each Covered Individual must sign and date their
respective certification statement:
I, [Full Name and Title], understand that I have been designated as a covered individual by
the Federal funding agency.
I certify to the best of my knowledge and belief that the information contained in this
Current and Pending Support Disclosure Statement is true, complete, and accurate. I
understand that any false, fictitious, or fraudulent information, misrepresentations, half-
truths, or omissions of any material fact, may subject me to criminal, civil, or administrative
penalties for fraud, false statements, false claims, or otherwise. (18 U.S.C. §§ 1001 and 287,
and 31 U.S.C. §§ 3729-3733 and 3801-3812). I further understand and agree that (1) the
statements and representations made herein are material to DOE’s funding decision, and (2)
I have a responsibility to update the disclosures during the period of performance of the
award should circumstances change which impact the responses provided above.
I also certify that, at the time of submission, I am not a party in a malign foreign talent
recruitment program. I further understand should I take action to involve myself with a
Malign Foreign Talent Recruitment Program during the period of performance of the award,
I must notify the Awardee’s Authorized Agent immediately, but no later than five business
days of taking such action and immediately recuse myself from all DOE awards.
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and cannot be sufficiently mitigated, DOE may withhold or recapture a Payment or terminate the
OT Agreement.
DOE’s decision regarding a due diligence review is not appealable.
Article 5.6: Performance of Work in the United States
All work in performance of the Technical Objectives must be performed in the United States
(i.e., the Awardee must expend 100% of the total project cost in the United States), unless the
Awardee requests and receives advance written authorization from the AO to perform certain
work outside of the United States. This provision does not apply to the acquisition of materials or
components of the Awardee’s project unless otherwise instructed in writing by the AO.
Article 5.7: Conflicts of Interest
The Awardee shall adopt and maintain a conflict of interest4 policy under this Agreement, which
shall be provided to DOE upon request. This conflict of interest policy shall address both
individual conflicts of interest as related to Key Personnel and Organizational Conflicts of
Interest, including but not limited to financial conflicts of interest (FCOI) (i.e., managed and
unmanaged/unmanageable).
The Awardee shall disclose in writing to the AO any potential or actual Conflict of Interest as
soon as reasonably practical after discovery thereof. The Awardee and the DOE shall jointly
develop a mitigation plan to address Conflicts of Interest as they arise.
SECTION VI: Reporting Requirements and Approvals
Article 6.1: Technical Objectives
1. The Awardee shall be responsible for the on-time delivery and satisfactory completion of
all Technical Objectives. Unless otherwise specified by the AO, in writing, in advance of
the deadline, all documents, including reports, are to be furnished to the DOE via email to
the AO.
SECTION VII: Modifications, Disputes, Termination, and Assignment
Article 7.1: Modifications
Proposals for modifications will be documented in writing and submitted by the Awardee to the
AO, or by DOE to the Awardee. Parties may request the technical, schedule, and financial
impact of the proposed modification as appropriate.
Any modification, including changes to the Technical Objectives, to this Agreement shall be
executed in writing and signed by an authorized representative of DOE and the Awardee. The
DOE is not obligated to pay for costs related to modifications. Further, the Awardee is not
4 See https://www.energy.gov/management/department-energy-interim-conflict-interest-policy-requirements-
financial-assistance
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obligated to accomplish work prior to mutual agreement between the AO and the Awardee and
performs work at risk of not being compensated without mutual agreement, in writing.
For minor or administrative modifications (e.g., changes to the paying office or appropriation
data), Awardee approval is not required, unless Awardee timely objects, in writing, to such a
proposed minor or administrative modification. In the event of any objection, the modification
must be bilaterally executed.
Article 7.2: Disputes
A. A party that believes there is a dispute about an issue under this Agreement will submit to
the other party, via email with receipt acknowledged by the other party, a summary of the
dispute. Disputes will be raised by the Awardee to the AO or by the AO to the Awardee
point of contact. The AO and awardee will work together to resolve issues or disputes,
including using any alternative dispute mechanisms to the maximum extent practicable.
Informal resolution, including resolution through an alternative dispute resolution
mechanism, will be preferred over formal procedures, to the extent practicable.
B. DOE Final Determination. If a dispute is not resolved informally between the DOE AO
and the Awardee, DOE shall provide a written determination signed by the AO, setting
forth DOE’s final disposition of such dispute. The determination will include a summary
of the dispute and the factual, legal, and if appropriate, policy reasons for DOE’s
disposition of the dispute.
C. Right of Appeal:
a. The final determination under this Article may only be appealed to the cognizant
Senior Procurement Executive (SPE), as defined by 41 U.S.C. 1702(c) for the
following actions:
i. A DOE determination that the awardee has failed to comply with the
applicable requirements of the Agreement;
ii. termination of an award, in whole or in part, by DOE;
iii. the application of DOE of an indirect cost rate; and
iv. DOE disallowance of costs.
b. The appeal must be received by DOE within 90 days of receipt of the final
determination. The mailing address for the DOE SPE is 1000 Independence Ave.,
SW., Washington DC 20585.
c. In reviewing disputes authorized under this article, the SPE will be bound by the
applicable law, statutes, and rules, including the requirements of this Article, and
by the terms and conditions of this Agreement.
d. The decision of the SPE shall be the final decision of DOE.
Article 7.3: Termination
A. Noncompliance. In the event the Awardee failed to comply with the terms and conditions
of this Agreement, the AO shall give written notice to the Awardee to comply, including the
factual and legal basis for the determination of noncompliance, the corrective actions, and the
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date by which they must be taken (not less than 30 days), and which of the actions authorized
under 2 CFR 930.345(a) DOE may take if the Awardee does not achieve compliance within the
time specified in the written notice. Failure to comply may result in termination of this
Agreement;
B. The Parties may mutually agree to terminate in whole or in part this Agreement by
providing at least 30 days advance written notice to the other party, provided such notice is
preceded by consultation between the Awardee and DOE and a reasonable determination by
either party that this Agreement will not produce beneficial results commensurate with the
expenditure of resources. Awardee and DOE will negotiate the termination conditions, including
the effective date and, in the case of partial termination, the portion to be terminated. If either the
Awardee or the DOE determines in the case of partial termination that the reduced or modified
portion of the Agreement will not accomplish its intended purpose, the Agreement may be
terminated in its entirety;
B. DOE may terminate the agreement in whole or in part if the Awardee materially fails to
comply with the articles or terms and conditions of an agreement, whether stated in a Federal
statute, regulation, assurance, application, plan, or the notice of award fails to comply with the
articles and requirements of the Agreement. DOE shall promptly provide Awardee with a Cure
Notice identifying the Material Breach. Within ten (10) days after receipt of the Cure Notice,
Awardee shall respond in writing to the Cure Notice and provide DOE with a Cure Plan;
C. DOE may terminate the agreement if the Awardee files a bankruptcy petition that is not
dismissed within ten (10) business days, the Awardee is adjudicated bankrupt or is otherwise
insolvent, or the Awardee ceases to do business or otherwise terminates its business operations;
and
D. DOE may terminate the agreement if the Awardee fails to achieve a Technical Objectives
within the Performance Period of the Agreement.
E. Either party may terminate without cause upon thirty (30) days written notice to the other
party. If DOE terminates without cause, the Awardee shall submit an invoice to DOE based on
the prorated fixed price for any remaining Milestone and such proration will be based on effort
expended from the last Milestone payment up to the point of termination. The AO, in their
discretion, will determine if and how much of an appropriate prorated payment is warranted.
Selectee acknowledges and agrees that no other compensation, of any nature or type, shall be
payable hereunder following the termination of this Agreement.
F. In the event of termination, DOE and the Awardee shall meet to affect an orderly
termination of any ongoing or planned activities and will negotiate in good faith for the
disposition of the Awardee’s Limited Rights Data in the possession of DOE, if applicable. In no
case will the Government be liable for expenses or other financial obligations of the Awardee
incurred due to early termination.
G. In the event this Agreement is terminated for any reason under paragraphs A-D of this
Article, DOE has the right to unilaterally not pay the Awardee. Mutual termination does not
impose any requirement that DOE pay in whole or part for Budget Periods not started.
9

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H. In the event of any termination by DOE, neither DOE nor the Awardee shall be liable for
any loss of profits, revenue, or any indirect or consequential damages incurred by the other Party,
its contractors, subcontractors, or customers as a result of any termination of this Agreement.
DOE’s or the Awardee’s liability for any damages under this Agreement is limited solely to direct
damages, incurred by the other Party, as a result of any termination of this Agreement subject to
mitigation of such damages by the complaining party. However, in no instance shall DOE's
liability for termination exceed the total amount of the Agreement.
Article 7.4 Assignment
Neither this Agreement nor any interest arising under it will be assigned by the Awardee or DOE
without the express written consent of the other party.
SECTION VIII: Miscellaneous
Article 8.1: Export Control
Awardee is responsible for ensuring compliance with all applicable United States Export Control
laws and regulations relating to any work performed under a resulting Agreement. The Awardee
must immediately report to DOE any export control violations related to this Agreement and
provide the corrective action(s) to prevent future violations.
Article 8.2: Communication
A. Administration. Unless otherwise provided in this Agreement, approvals permitted or
required to be made by DOE, or any other document that binds the government, may be
made only by the DOE Agreements Officer who has a warrant including the award of
Other Transaction Agreements. Administrative and contractual matters under this
Agreement shall be referred to the following representatives of the Parties:
DOE Agreements Officer: XXXX
DOE Grants and Agreements Specialist: XXXX
DOE Patent Counsel: Michael Dobbs, (331) 465-1317, mike.dobbs@science.doe.gov (for
all questions regarding Intellectual Property matters)
Awardee: XXXX
B. Technical Objectives. Technical Objectives under this Agreement shall be referred to the
following representative:
DOE Program Manager: XXXX
Awardee: XXXX
C. Technical. Technical matters under this Agreement shall be referred to the following
representatives:
10

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DOE Program Manager: XXXX
Awardee: XXXX
D. Change of Designated Representative. Each party may change its representatives named
in this Article by written notification to the other party.
Article 8.3: Closeout
Upon Agreement completion, the AO must close out the Agreement in accordance with the
Federal-Wide Research Terms and Conditions and DOE Standard Research Terms and
Conditions Agency Specific Requirements, Financial Assistance Handbook Chapter 30A -
Closeout of Financial Assistance Instruments, 2 CFR § 930, and the DOE Office of Scientific
and Technical Information (OSTI) E-Link website (https://www.osti.gov/elink/), including
ensuring all deliverables have been accepted and any audits completed. The Awardee agrees to
provide DOE all relevant documents requested to closeout this Agreement.
Article 8.4: Resolution of Conflicting Conditions
Any apparent inconsistency between Federal statutes and regulations and the terms and
conditions contained in this Agreement must be referred to the AO for guidance.
Article 8.5: Suspension and Debarment
The Awardee shall not contract with entities that are currently listed on the System for Award
Management as suspended or debarred in support of this Agreement. In accordance with
Executive Orders 12549 and 12689, the regulations at 2 CFR part 180, Guidance for
Governmentwide Debarment and Suspension (Non procurement) are applicable to this
Agreement.
Article 8.6: Inspector General
A. The Awardee is required to provide any information, documents, site access, or other
assistance requested by DOE or Federal auditing agencies (e.g., DOE Inspector General,
Government Accountability Office, Defense Contracting Audit Agency, United States Digital
Service, Department of Government Efficiency) for work related to this Agreement. Single
Audits are not required under this Agreement.
B. In the event that the Awardee has Federal funding from another Federal agency or entity, in
accordance with 42 U.S.C. § 7137, the Comptroller General of the United States, or any of
his duly authorized representatives, shall have access to and the right to examine any books,
documents, papers, records, or other recorded information of the Awardee of Federal funds or
assistance under this Agreement, including contracts between the Awardee and other entities.
Article 8.7: System for Award Management
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The Awardee is required to be registered in the System for Award Management (SAM,
www.sam.gov). The Awardee agrees to opt in to SAM notifications and monitor SAM for DOE
issued information.
Article 8.8: Insolvency, Bankruptcy or Receivership
A. The Awardee shall immediately notify DOE of the plans for, or significant likelihood of, any
of the following events: (i) the Awardee or the Awardee’s parent’s filing of a voluntary case
seeking liquidation or reorganization under the Bankruptcy Act; (ii) the Awardee’s consent to
the institution of an involuntary case under the Bankruptcy Act against it or its parent; (iii)
the filing of any similar proceeding for or against the Awardee or the Awardee’s parent, or its
consent to, the dissolution, winding-up or readjustment of its debts, appointment of a
receiver, conservator, trustee, or other officer with similar powers over it, under any other
applicable state or federal law; or (iv) the Awardee’s insolvency due to its inability to pay its
debts generally as they become due.
B. Such notification shall be in writing and shall: (i) specifically set out the details of the
occurrence of an event referenced in paragraph A; (ii) provide the facts surrounding that
event; and (iii) provide the impact such event will have on the project being funded by this
Agreement.
C. Upon the occurrence of any of the four events described in the first paragraph, DOE reserves
the right to conduct a review of the Awardee’s agreement to determine their compliance with
the required elements of the agreement. If the DOE review determines that there are
significant deficiencies or concerns with the Awardee’s performance under the agreement,
DOE reserves the right to impose additional requirements, as needed.
D. Failure of the Awardee to comply with this term may be considered a Material Breach of this
Agreement.
Article 8.9: Fraud, Waste and Abuse
The Awardee shall disclose, in a timely manner, in writing to DOE all violations of Federal
criminal law involving fraud, bribery, or gratuity violations by Awardee, its employees, and
agents that potentially affect the award. Compliance with the Rules of Professional Conduct does
not violate this paragraph. The DOE Office of Inspector General maintains a Hotline for
reporting allegations of fraud, waste, abuse, or mismanagement. To report such allegations,
please visit https://www.energy.gov/ig/ig-hotline.
Article 8.10: Choice of Law
The Awardee acknowledges that this Agreement is subject to Federal law of the United States.
Article 8.11 NONDISCLOSURE AND CONFIDENTIALITY AGREEMENTS
ASSURANCES
A. By entering into this Agreement, the Awardee does not and will not require its employees
or contractors to sign internal nondisclosure or confidentiality agreements or statements
prohibiting or otherwise restricting its employees or contactors from lawfully reporting
waste, fraud, or abuse to a designated investigative or law enforcement representative of a
12

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federal department or agency authorized to receive such information.
B. The undersigned further attests that Awardee will not implement or enforce any
nondisclosure and/or confidentiality policy, form, or agreement it uses in the performance
of the Technical Objectives unless it contains the following provisions:
(1) These provisions are consistent with and do not supersede, conflict with, or
otherwise alter the employee obligations, rights, or liabilities created by existing
statute or Executive order relating to: (a) classified information, (b) communications
to Congress, (c) the reporting to an Inspector General or the Office of Special
Counsel of a violation of any law, rule, or regulation, or mismanagement, a gross
waste of funds, an abuse of authority, or a substantial and specific danger to public
health or safety, or (d) any other whistleblower protection. The definitions,
requirements, obligations, rights, sanctions, and liabilities created by controlling
Executive orders and statutory provisions are incorporated into this agreement and
are controlling.
(2) The limitation above shall not contravene requirements applicable to Standard Form
312, Form 4414, or any other form issued by a Federal department or agency
governing the nondisclosure of classified information.
C. Notwithstanding provision listed in paragraph (A), a nondisclosure or confidentiality
policy form or agreement that is to be executed by a person connected with the conduct of
an intelligence or intelligence-related activity, other than an employee or officer of the
United States Government, may contain provisions appropriate to the activity for which
such document is to be used. Such form or agreement shall, at a minimum, require that the
person will not disclose any classified information received during such activity unless
specifically authorized to do so by the United States Government. Such nondisclosure or
confidentiality forms shall also make it clear that they do not bar disclosures to Congress,
or to an authorized official of an executive agency or the Department of Justice, that are
essential to reporting a substantial violation of law.
Article 8.12 CORPORATE FELONY CONVICTION AND FEDERAL TAX LIABILITY
ASSURANCES
By entering into this Agreement, the undersigned attests that the Awardee has not been
convicted of a felony criminal violation under Federal law in the 24 months preceding the
date of signature.
The undersigned further attests that Awardee does not have any unpaid Federal tax liability
that has been assessed, for which all judicial and administrative remedies have been exhausted
or have lapsed, and that is not being paid in a timely manner pursuant to an agreement with
the authority responsible for collecting the tax liability.
For purposes of these assurances, the following definitions apply: A Corporation includes any
entity that has filed articles of incorporation in any of the 50 states, the District of Columbia,
or the various territories of the United States [but not foreign corporations]. It includes both
for- profit and non-profit organizations.
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Article 8.13 LOBBYING RESTRICTIONS
The Awardee agrees that none of the funds under this Agreement shall be expended, directly
or indirectly, to influence congressional action on any legislation or appropriation matters
pending before Congress, other than to communicate to Members of Congress as described in
18 U.S.C. § 1913. This restriction is in addition to those prescribed elsewhere in statute and
regulation.
Article 8.14 INDEMNIFICATION
The Awardee will indemnify the Government and its officers, agents, or employees for any
and all liability, including litigation expenses and attorneys' fees, arising from suits, actions,
or claims of any character for death, bodily injury, or loss of or damage to property or to the
environment, resulting from the project, except to the extent that such liability results from
the direct fault or negligence of Government officers, agents or employees, or to the extent
such liability may be covered by applicable allowable costs provisions.
Article 8.15 STATEMENT OF FEDERAL STEWARDSHIP
DOE will exercise normal Federal stewardship in overseeing the project activities performed
under this Agreement. Stewardship activities include, but are not limited to, conducting site
visits; reviewing performance and financial reports; providing technical assistance and/or
temporary intervention in unusual circumstances to correct deficiencies which develop during
the project; assuring compliance with terms and conditions; and reviewing technical performance
after project completion to ensure that the Technical Objectives have been accomplished.
Article 8.16 SITE VISITS
DOE authorized representatives have the right to make site visits at reasonable times to review
project accomplishments and management control systems and to provide technical assistance, if
required. You must provide, and must require your subrecipients to provide, reasonable access
to facilities, office space, resources, and assistance for the safety and convenience of the
government representatives in the performance of their duties. All site visits and evaluations
must be performed in a manner that does not unduly interfere with or delay the work.
SECTION IX: DEFINED TERMS
In addition to terms defined in other provisions of this Agreement, the terms and phrases as
defined below, when used herein as the defined term shall have the following meanings:
“Allowable Cost” shall mean a cost incurred by a recipient that is: (1) reasonable for the
performance of the award; (2) allocable; (3) in conformance with any limitations or exclusions
set forth in the Federal cost principles applicable to the organization incurring the cost or in the
Agreement documents as to the type or amount of cost; (4) consistent with regulations, policies,
and procedures of the recipient that are applied uniformly to both federally supported and other
activities of the organization; (5) accorded consistent treatment as a direct or indirect cost; (6)
determined in accordance with generally accepted accounting principles; and (7) not included as
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a cost in any other federally supported award (unless specifically authorized by statute). See 2
C.F.R. § 200.403.
“AO” shall mean DOE Agreements Officer.
“AI Artifact” shall mean any dataset, code, scripts, pipelines, models (including architectures,
parameters, and weights), prompts, outputs, documentation, metadata, test/evaluation suites,
synthetic data, and system/model/data cards created, used, or improved under the Project
Agreement.
“Covered Individual” means an individual who (a) contributes in a substantive, meaningful way
to the scientific development or execution of the scope of work of the agreement, and (b) is
designated as a covered individual by DOE. DOE designates as covered individuals any principal
investigator (PI); project director (PD); co-principal investigator (Co-PI); co-project director
(Co-PD); project manager; and any individual regardless of title that is functionally performing
as a PI, PD, Co-PI, Co-PD, or project manager. Submission of a current and pending support
disclosure and/or biosketch/resume for a particular person serves as an acknowledgement that
DOE designates that person as a covered individual. DOE may further designate covered
individuals during the agreement period of performance.
“Curated Data” shall mean any Data generated in Milestone 2.
“Data” shall mean recorded information, regardless of form or the media on which it may be
recorded, including Technical Data. The term does not include information incidental to Other
Transaction (OT) Agreement administration, such as financial, administrative, cost or pricing or
management information.
“Data and Intellectual Property Rights Certification” shall mean a formal written statement,
signed by an authorized representative of the Awardee, delivered as part of a Milestone, which
attests that the Awardee has the necessary legal and contractual rights to all data, software, and
intellectual property used in a delivered AI Artifact, sufficient to enable its deployment and use
as contemplated under this Agreement.
“Effective Date” shall mean the date upon which the Agreement will enter into force which will
be the date of the last signatory to the Agreement.
“Foreign Country of Concern” shall mean the People's Republic of China, the Russian
Federation, the Democratic People's Republic of Korea, the Islamic Republic of Iran, and
Belarus, or any other country determined to be a foreign country of concern by the Secretary of
State, and including countries of risk designated by DOE. This list is subject to change. DOE
reserves the right, in the exercise of its absolute discretion, to add or subtract any foreign country
determined to be of risk consistent with DOE policies by a unilateral amendment to this
Agreement.
“Foreign Entity” shall mean any corporation, business association, partnership, trust, society, or
any other entity or group that is not incorporated or organized to do business in the United States,
15

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as well as international organizations, foreign governments and any agency or subdivision of
foreign governments including state-owned enterprises. The term “Foreign Countries of
Concern” and “Foreign National” are separate terms and not necessarily covered under the
definition of a Foreign Entity although “Foreign Countries of Concern” are also “Foreign
Entities”.
“Foreign National” shall mean any individual other than a U.S. citizen.
“Government” shall mean the Federal government of the United States of America including its
agencies and departments.
“Intellectual Property” shall mean any and all subject inventions, copyrights, and trademarks
developed by an Awardee.
"Key Personnel” shall mean the voting board members, C-suite executives, and other senior
personnel identified by Awardee.
“Limited rights data” shall mean data developed at private expense and first produced outside
the performance of this agreement that embody trade secrets, or are commercial, or financial, and
confidential or privileged.
“Material Breach” shall mean a breach by the Awardee or DOE of any of its obligations under
this Agreement including those which result, or are likely to result, in the inability of the
Awardee or DOE to effectively comply with the terms of this Agreement.
“Model Data” shall mean any data generated in the performance of Milestone 3.
“Budget” shall mean the financial plan for the project or program that the DOE approves during
the Federal award process or in subsequent amendments to the Federal award. It may include the
Federal and non-Federal share or only the Federal share, as determined by DOE.
“Non-Public Data” shall mean Data that has not been publicly distributed to others without
restriction on further dissemination. Specifically excluded from this definition is data shared at a
conference, public talk or other public forum if the data shared is shared without restriction on
further dissemination.
“Organizational Conflicts of Interest” shall mean where the Awardee is unable, or appears to be
unable, to be impartial in conducting activities under this Agreement due to its relationships with
affiliates, organizations, or any other interested party.
“Project Agreement” means an agreement between the Awardee and their partners/collaborators
in the performance of the Technical Objectives, which is intended to address all data rights,
including allocation of rights to Project AI Artifacts. The Project Agreement includes
agreements with DOE National Laboratory, preferably using the preapproved AI Bridge
Agreement containing a Data Use Agreement (DUA) template that complies with Article 3.2.
“Project AI Artifact” shall mean AI Artifacts developed in performance of the Technical
Objectives, including a Project Agreement.
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“Technical Objectives” shall mean the objectives identified in Appendix I – Technical
Objectives.
“Unlimited Rights” shall mean the right of the Government to use, disclose, reproduce, prepare
derivative works, distribute copies to the public, and perform publicly and display publicly, in
any manner and for any purpose, and to have or permit others to do so.
IN WITNESS WHEREOF, the Parties have executed this Agreement.
U.S. DEPARTMENT OF ENERGY AWARDEE
By:_____________________________ By: ______________________________
[NAME] [NAME]
Agreements Officer TITLE
Dated: _______________________________ Dated: _______________________________
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APPENDIX I
Technical Objectives, Reporting, Deliverables
Technical Technical Technical Objective Data Protection Quarter Due Federal Awardee Cost Share
Objectives# Objective Description (from project Share ($)
Title start)
1 Agreements The execution of all All data delivered to 1
and agreements within the DOE must be
Alignment Application Team within 90 delivered with
days of award. unlimited rights.
1. DOE must be provided
with a copy of the The Awardee may
executed Project redact business
Agreement showing sensitive portions of
execution by at least the the Project
Application Team and Agreement that are
meeting all not relevant to
requirements of the confirming
award, including execution of the
requirements listed in agreement amongst
Article 3.2(1)(d). The the party and
Team is encouraged to compliance with the
leverage templates award terms.
provided by DOE,
especially when
working with a DOE
Laboratory.
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Technical Technical Technical Objective Data Protection Quarter Due Federal Awardee Cost Share
Objectives# Objective Description (from project Share ($)
Title start)
2 Data The curation of all scientific All data delivered to
Curation data within the Application DOE may be
Team so that the data is identified as
structured, cleaned, and Protected Data
preprocessed in a way that except:
makes it suitable for use in 1.a summary
artificial intelligence and report; and
machine learning models no 2.(a) general test
later than XXX. or performance
1. The curated data must results
include [Insert a demonstrating
Specific description of technical
the data to be curated] breakthroughs,
2. The curated data will be Technical
delivered to [Insert Objectives or
Where Curated Data achievements; (b)
must be delivered. This general data
should typically include demonstrating
delivery to the progress toward
American Science meeting DOE's
Cloud] with the technical targets and
following rights: (c) any research
[Insert Specific Data data contained in
rights approved by publications
cognizant DOE Patent resulting from the
Counsel]
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Technical Technical Technical Objective Data Protection Quarter Due Federal Awardee Cost Share
Objectives# Objective Description (from project Share ($)
Title start)
3. The curated data must: work under the
[Insert specific agreement.
requirements]
[including adherence to
data quality standards
as may be established
by the National Science
and Technology
Council (NSTC)
Machine Learning and
AI Subcommittee or
other relevant Federal
guidance, to ensure
suitability for artificial
intelligence and
machine learning
models, and specifically
for use in developing
next-generation
microelectronics and
accelerating innovation
in discovery science
and engineering for
new energy
technologies via the
American science
20

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Technical Technical Technical Objective Data Protection Quarter Due Federal Awardee Cost Share
Objectives# Objective Description (from project Share ($)
Title start)
cloud, while
maintaining appropriate
privacy and
confidentiality
protections.]
4. As a condition of
Technical Objective
completion, the
Awardee shall deliver
to the Agreements
Officer (AO) a signed
Data and Intellectual
Property Rights
Certification for the
delivered model and all
its constituent
components. This
certification shall, at a
minimum, attest to the
following:
a.That the Awardee has
conducted a thorough review
of all datasets, software
libraries, pre-trained models,
and any other intellectual
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Technical Technical Technical Objective Data Protection Quarter Due Federal Awardee Cost Share
Objectives# Objective Description (from project Share ($)
Title start)
property (collectively, “Model
Components’) used in the
creation, training, and
operation of the delivered AI
model.
b.That the Awardee possesses
sufficient rights to all Model
Components to permit the
deployment of the model on
the American Science Cloud,
consistent with the terms of
this Agreement, the associated
Project Agreement, and the
requirements of Public Law
119-21, as applicable.
c.That the deployment and use
of the model as described in
this Agreement does not
infringe upon any third-party
patent, copyright, trade secret,
or other intellectual property
right.
5. d. That all data
sourcing and licensing
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Technical Technical Technical Objective Data Protection Quarter Due Federal Awardee Cost Share
Objectives# Objective Description (from project Share ($)
Title start)
documentation has been
archived and is
available for inspection
by DOE upon request.
3 Creation of Creation of artificial All data delivered to
an Artificial intelligence models no later DOE may be
Intelligence than XXX. identified as
Model 1. The delivered model Protected Data
must have been created except:
using the data curated 1.a summary of the
in Technical Objective report; and
two. 2.(a) general test
2. The model must or performance
perform the following results
functionality: [INSERT demonstrating
SPECIFIC DETAILS technical
ON THE breakthroughs,
PERFORMANCE Technical
CHARACTERISTICS Objectives or
OF A SUCCESSFUL achievements; (b)
MODEL] [and general data
demonstrate capabilities demonstrating
for interpretability, progress toward
control, and robustness meeting DOE's
against adversarial technical targets and
attacks, as well as (c) any research
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Technical Technical Technical Objective Data Protection Quarter Due Federal Awardee Cost Share
Objectives# Objective Description (from project Share ($)
Title start)
incorporating secure- data contained in
by-design principles to publications
mitigate risks of misuse resulting from the
or unauthorized access. work under the
Furthermore, the agreement.
models shall be
designed for seamless
integration with and
provision to the
scientific community
through the American
science cloud, adhering
to its operational and
data sharing standards
to accelerate innovation
in discovery science
and engineering for
new energy
technologies.]
3. The model will be:
a. provided to the
scientific
community
through the
American
science cloud to
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[Document continues — 33 more pages]

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> Download XLSX file: Genesis Mission Phase I Application Template.xlsx

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