US DOE: The Genesis Mission: Transforming Science and Energy with AI

Sponsor: US DOE
Internal Deadline: 03/27/2026
Institutional Submission Limit: See below
Sponsor Deadline: 04/28/2026
Program Website

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.

The Genesis Mission 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.  

 Selected applicants: 

• 1B: AI-Driven Matierals Processing (BES), Lang Yuan


• 1D: Digitization of Industrial Processes (ITO), Jay Gaillard


• 1E: AI-Enabled Smart Manufacturing (AMMTO), Thor Wuest


• 1F:  Energy Material Manufacturing (AFFO), Paul Ziehl


• 2D: Bio Design (BER), Tao Wei


• 3B: AI-Enabled Materials Discovery and Engineering (AMMTO), Ming Hu


• 3F: Connections for Isolation (BES), Zhenmeng Peng


• 3D: Extraction and Processing Technologies (AMMPTO, AMMTO), Biplav Srivastava


• 4A: Accelerated Nuclear Power Plant Design and Licensing, Ruixian Fang


• 4B: Autonomous Power Plant Operations, Austin Downey


• 4C: AI-Assisted Manufacturing and Construction, Travis Knight


• 4F: AI-Assisted Site Characterization, Sourav Banerjee


• 6C: Treatment Process Optimization (EM-3.2, ASCR), Hanno zur Loye


• 8C: AI for Quantum Imaging and Sensing (HEP, NP), Thomas Crawford


• 9D: 3D Non-Volatile Compute-in-Memory Technology (ASCR), Ramtin Zand


• 9H: Power Electronics and Communication Networks (ASCR), Sai Mu


• 10A: Data Center Load Flexibility, Behrooz Mirafzal


• 10B: Data Center Thermal Management (ITO), Dongkyu Lee


• 11A: Advanced Robotics for Dynamic Laboratory Environments (ASCR), Vignesh Narayanan


• 12A: Functional to Quantam Materials (BES), Rongying Jin


• 12B: Structural Materials (BES, FES, AMMTO), Kevin Huang


• 12E: Targetry by Design (IRP), REMOVED BY FUNDING AGENCY


• 12F: AI-Enabled Materials Discovery, Development, and Qualification (AMMTO), Morgan Stefik


• 14A: Foundation Models of Particle Interactions and Cosmic Physics (HEP, NP), Alexey Petrov


• 14C: Expedited Discovery from High Complexity and Petabyte-Scale Datasets (HEP, NP), David Tedeschi


• 15B: Water and Energy (BER), Jasim Imran


• 16B: Grid Operations Optimization (OE, CMEI-IESO, SC-ASCR), Necmi Altin


• 17A: Chemical and Hydrologic Transport in Subsurface (BER) Hong Wang

Challenge areas

Please see program website for focus areas listed under each of the challenge areas listed below; there are up to 99 available focus areas:

• Reimagining Construction and Operation of Buildings


• Scaling the Biotechnology Revolution


• Securing America’s Critical Minerals Supply


• Delivering Nuclear Energy that is Faster, Safer, Cheaper


• Accelerating Delivery of Fusion Energy


• Transforming Nuclear Cleanup and Restoration


• Discovering Quantum Algorithms with AI


• Realizing Quantum Systems for Discovery


• Recentering Microelectronics in America


• Securing U.S. Leadership in Data Centers


• Accelerating Materials Discovery, Production, and Qualification for Strategic Deterrence


• Achieving AI-Driven Autonomous Laboratories


• Designing Materials with Predictable Functionality


• Enhancing Particle Accelerators for Discovery


• Unifying Physics from Quarks to the Cosmos


• Predicting U.S. Water for Energy


• Scaling the Grid to Power the American Economy


• Unleashing Subsurface Strategic Energy Assets


• Accelerating Nuclear Threat Assessment, Preparedness, and Response


• Harnessing America’s Historic Nuclear Data and Research


• Increasing Experimental Capacity at Nuclear Research Facilities


• Integrating Design and Production Operations for Nuclear Deterrence


• Safeguarding Nuclear Materials from Proliferation Threats


• Streamlining Production, Removing Red Tape, and Ensuring Safety in the Nuclear Enterprise


• Strengthening Deterrence Through Attribution of Nuclear and Radiological Signatures

Phased Program Structure:

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.

 

Institutional Limitations:

Applicant institutions are limited to no more than one application as the lead institution per focus area (potentially up to 99)  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.

Important 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.

 

Important Dates:

Submission Deadline for FY26 Phase I Applications: April 28, 2026, at 11:59 p.m. Submission Deadline for FY26 Phase II Letters of Intent: April 28, 2026, at 5 p.m. Submission Deadline for FY26 Phase II Applications: May 19, 2026, at 11:59 p.m. Submission Deadline for Phase II Applications resulting from FY26 Phase I Awards: December 17, 2026, at 11:59 p.m.

Submission Process

Limited submissions MUST be coordinated with the Office of the Vice President for Research.

• one-page project abstract (include at top the challenge area, project title, PI, expected industry/national laboratory partners, and list of key collaborators)
• 2-3 page CV/biosketch for the PI (SCiENcv preferred)