DE-FOA-0003612.000001.pdf

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: Amendment 000001

Assistance Listings: 81.049

RFA Issue Date: March 17, 2026 Submission Deadline for FY26 Phase I Applications:

April 28, 2026, at 11:59 PM Eastern

Submission Deadline for FY26 Phase II Letters of Intent:

April 28, 2026, at 5 PM Eastern

Submission Deadline for FY26 Phase II Applications:

May 19, 2026, at 11:59 PM Eastern

Submission Deadline for Phase II Applications resulting from FY26 Phase I Awards:

December 17, 2026, at 11:59 PM Eastern

Amendment 000001 is issued on April 10, 2026, with the following changes:

• Added instructions for Phase II LOIs.

• Changed LOI responses from “not provided” to “at the discretion of DOE”.

• Added instructions for Phase II applications.

• Added a small number of questions for merit reviewers to consider for Phase II applied technology applications.

• Added “per year” to “Envisioned as 3 to 5 times the Phase I award per year” in all cases where it is listed. Previously, it was listed in only one location.

• Added language that clarified that Phase II budgets both above and below the 3 to 5 times the Phase I award are allowed when appropriate.

• Added the possibility of a Phase II midterm review.

• Set a Phase II award start date that applicants can use when preparing budgets.

• Added program policy factor, “Availability of computational resources”

• Removed LM and IRP as participating offices from the following topic areas because they have no associated funding in this solicitation: 6-A, 12-E, 13-A. This resulted in focus area 12-E being removed.

• Added the statement: “Phase II applications will not be accepted in this focus area.” to the following focus areas: 1-D, 1-E, 1-F, 2-E, 3-B, 3-C, 9-A, 10-A, 10-B, 12-F, 12-G, and 21-C.

Table of Contents

TABLE OF CONTENTS

I. BASIC INFORMATION

EXECUTIVE SUMMARY

FUNDING DETAILS

KEY FACTS

KEY DATES

SUBMISSION TEAMING REQUIREMENTS

AGENCY CONTACT INFORMATION

INFORMATIONAL WEBINAR/OFFICE HOURS

RECOMMENDATION

II. ELIGIBILITY

A. ELIGIBLE APPLICANTS

B. COST SHARING

C. ELIGIBLE INDIVIDUALS

D. LIMITATIONS ON SUBMISSIONS

E. OTHER ELIGIBILITY REQUIREMENTS

III. PROGRAM DESCRIPTION

A. PURPOSE

B. PROGRAM GOALS, OBJECTIVES, AND PRIORITIES

C. AWARD CONTRIBUTION TO GOALS AND OBJECTIVES

D. PERFORMANCE GOALS

E. PROGRAM UNALLOWABLE COSTS

F. CITATIONS TO STATUTE AND REGULATIONS

G. PROGRAM HISTORY

H. OTHER INFORMATION

IV. APPLICATION CONTENTS AND FORMAT

A. PRELIMINARY SUBMISSIONS

B. APPLICATION

C. COMPONENT PIECES OF THE APPLICATION

D. INFORMATION THAT MUST BE SUBMITTED AFTER APPLICATION BUT

BEFORE AWARD

V. SUBMISSION REQUIREMENTS AND DEADLINES

A. ADDRESS TO REQUEST APPLICATION PACKAGE

B. UNIQUE ENTITY IDENTIFIER (UEI) AND SYSTEM FOR AWARD MANAGEMENT

(SAM.GOV)

C. SUBMISSION INSTRUCTIONS

D. SUBMISSION DATES AND TIMES

VI. APPLICATION REVIEW INFORMATION

A. RESPONSIVENESS REVIEW

B. REVIEW CRITERIA

C. REVIEW AND SELECTION PROCESS

VII. AWARD NOTICES

A. TYPE OF AWARD INSTRUMENT

B. ANTICIPATED TIMELINE FOR NOTICE OF SELECTION FOR AWARD

NEGOTIATION

VIII. POST-AWARD REQUIREMENTS AND ADMINISTRATION

A. ADMINISTRATIVE AND NATIONAL POLICY REQUIREMENTS

B. REPORTING

C. REPORTING OF MATTERS RELATED TO RECIPIENT INTEGRITY AND

PERFORMANCE (DECEMBER 2015)

D. INTERIM CONFLICT OF INTEREST POLICY FOR FINANCIAL ASSISTANCE

IX. OTHER INFORMATION

A. CHECKLIST FOR AVOIDING COMMON ERRORS

B. HOW-TO GUIDES

C. ADMINISTRATIVE AND NATIONAL POLICY REQUIREMENTS

D. REFERENCE MATERIAL

I. Basic Information

United States (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 fiscal year (FY)26 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 Letter of Intent (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.

https://www.energy.gov/documents/genesis-mission-science-and-technology-challenges?utm_medium=email&utm_source=govdelivery https://www.energy.gov/documents/genesis-mission-science-and-technology-challenges?utm_medium=email&utm_source=govdelivery

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 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 and Phase II 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 per year.

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 Amendment 000001 Assistance Listing 81.049 Statutory Authority The programmatic authorizing statutes are:

1 More information on the Genesis Mission Consortium is available at https://www.genesismissionconsortium.org/ 2 42 U.S.C. § 7256(a), (g) (1993) https://www.genesismissionconsortium.org/

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 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 Regulations

Uniform Administrative Requirements, Cost Principles, and Audit Requirements for Federal Awards, codified at 2 C.F.R. § 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 Customer Support

1-800-518-4726 (toll-free) 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.

mailto:support@grants.gov mailto:GenesisMissionNOFO@science.doe.gov 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.

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.

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 percent cost share for both basic and applied R&D activities and 50 percent 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 percent 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, § 988.

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 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 percent 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.

https://science.osti.gov/User-Facilities

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 Pub. L. No.

119-21 § 50404 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.

https://www.energy.gov/documents/genesis-mission-science-and-technology-challenges?utm_medium=email&utm_source=govdelivery https://www.energy.gov/documents/genesis-mission-science-and-technology-challenges?utm_medium=email&utm_source=govdelivery https://www.whitehouse.gov/presidential-actions/2025/11/launching-the-genesis-mission/ https://www.energy.gov/articles/energy-department-advances-investments-ai-science https://www.energy.gov/documents/genesis-mission-science-and-technology-challenges?utm_medium=email&utm_source=govdelivery https://www.energy.gov/documents/genesis-mission-science-and-technology-challenges?utm_medium=email&utm_source=govdelivery

Phased Program Structure

Projects funded under this RFA 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 three to five times the initial phase per year. Deviations above and below this level are expected and will be made when appropriate to the scope of work.

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.

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.

https://genesismissionconsortium.org/

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. The following focus areas are not accepting Phase II applications: 1-D, 1-E, 1-F, 2-E, 3-B, 3-C, 9-A, 10-A, 10-B, 12-F, 12-G, and 21-C. This requirement is listed in the relevant focus area descriptions. Cost share requirements are specific to each focus areas.

https://genesismissionconsortium.org/

Note on funding or co-funding opportunities from the Office of Science-Advanced Scientific Computing Research (SC-ASCR) program:

Core components of ASCR’s mission relevant to this RFA 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 (HPC) through edge computing and laboratory/experimental automation. AI innovation is a priority for this RFA.

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 RFA 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); CMEI- Alternative Fuels and Feedstocks Office (CMEI-AFFO); 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.

Focus Areas for FY26:

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)

PHASE II APPLICATIONS WILL NOT BE ACCEPTED IN THIS FOCUS AREA.

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, 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 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.

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 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)

PHASE II APPLICATIONS WILL NOT BE ACCEPTED IN THIS FOCUS AREA.

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 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.

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.

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…