DOE Genesis Mission: Transforming Science and Energy with AI

Teaming and Concepts

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The U.S. Department of Energy (DOE)’s is a national effort to use artificial intelligence to accelerate scientific discovery, strengthen national security and drive energy innovation. The initiative is intended to connect advanced computing, data, scientific infrastructure, and research teams in ways that materially improve the speed and impact of research and development.

DOE has released the Funding Opportunity Announcement (FOA) , which seeks interdisciplinary teams to develop AI-enabled research and development workflows aligned with mission-relevant challenge areas. The opportunity is organized around 21 Challenge Areas and associated Focus Areas, with an emphasis on strong scientific merit, clear potential for measurable impact, and cross-sector teaming.

DOE is soliciting new FY26 Phase I small team and Phase II large team applications in the following areas: advanced manufacturing, biotechnology, critical materials, nuclear fission, nuclear fusion, quantum information science, semiconductors and microelectronics, discovery science and energy.

Submission Deadlines

  • Campus Teaming & Concepts RFI: March 27, 2026
  • Phase I Applications: April 28, 2026
  • Phase II Letters of Intent: April 28, 2026
  • Phase II Applications: May 19, 2026

Important Information

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 Facility,
  2. Industry, andÌý
  3. Institute of Higher Education (IHE)/Non-profit/Other.Ìý

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.Ìý

Campus Teaming & Concepts Request for Information (RFI)

The Research and Innovation Office (RIO) requested information on potential С»ÆÊé Boulder teams and concepts to assess institutional interest, identify areas of strength, and support coordination where appropriate. To assist with this effort, RIO prepared a spreadsheet listing the 21 Challenge Areas and related FY26 Focus Areas.

For each potential concept, the following information was provided:

  • PI name
  • Collaborators
  • National lab partner(s), if any
  • Industry partner(s), if any
  • Primary subtopic/focus area, identified by letter and full name
  • Secondary subtopic/focus area(s), if applicable
  • Any relevant dataset(s) or data asset(s), if applicableÌý

Contributors used the spreadsheet to share potential concepts, including teams that were still forming. RIO sought visibility into areas of interest, likely leaders, and possible teaming opportunities across campus. Preliminary information was welcomed, including early-stage concepts and relevant datasets or data resources that could strengthen a potential response. Given the FOA’s emphasis on AI-enabled workflows and measurable impact, visibility into distinctive data assets helped RIO assess institutional strengths and identify opportunities for cross-campus teaming.

RIO will use the responses, together with input from the campus’s Genesis task force, to help facilitate teaming and coordinate proposal development support. As appropriate, RIO will assist with team formation and proposal planning.

This request for information is now closed. The content above is retained for historical reference and to document the approach used to assess institutional strengths and coordinate across campus.

Partnership ServiceÌý

To support formation of strategic partnerships for Genesis Mission opportunities, the Genesis Mission Consortium launched the Partnership Exchange PortalÌýon March 19th and is the primary tool for team formation.Ìý

The Partnership Exchange is a platform that connects industry and academic organizations with DOE, the National Laboratories, and their resources to help facilitate collaboration for this RFA and future Genesis Mission opportunities.

Participation is optional, and consortium membership is not required to use the Partnership Exchange or to be eligible for funding under this RFA.

Prepare in Advance

To streamline participation and enable faster partner matching, prospective users are encouraged to prepare the following information in advance:

  • RFA topics and focus areas of interest
  • A short statement describing collaboration interests
  • A brief description of organizational capabilities and recent work, including links to relevant publications or projects

Additional information and access to the Partnership Exchange is available on the .

Remaining Subtopic Slots

The chart below identifies Genesis subtopic areas that currently remain open. PIs interested in leading a submission in one of these areas should email ltdsubs@colorado.edu. Subtopics will be filled on a rolling basis. If multiple expressions of interest are received for the same subtopic, RIO will determine next steps, which may include encouraging collaboration or conducting a brief internal review to select a lead PI.

1

Advanced Manufacturing

ÌýAÌýAgentic AI-Driven Chemical Manufacturing (BES)
ÌýCÌýAI-Enabled Manufacturing for Extreme Energy Systems (FES)
ÌýDÌýDigitalization of Industrial Processes (ITO)
ÌýFÌýEnergy Material Manufacturing (AFFO)

2

Biotechnology

CPredictive Engineering of Microbial Communities (BER)

4

Nuclear Energy

ÌýAÌýAccelerated Nuclear Power Plant Design and Licensing
ÌýBÌýAutonomous Power Plant Operations
ÌýCÌýAI-Assisted Manufacturing and Construction
ÌýDÌýAutonomous Research and Development
ÌýEÌýAccelerated Fuel Cycle Facility Design and Licensing to Secure the Domestic Fuel Supply
ÌýFÌýAI-Assisted Site Characterization
ÌýGÌýAI-Assisted End Disposition Design
ÌýHÌýAI/ML Tools for Review and Release of Legacy Documents

5

Fusion Energy

ÌýAÌýStructural Materials (FES)
ÌýBÌýPlasma-Facing Materials (FES)
ÌýCÌýAdvancing Confinement Approaches (FES)
ÌýDÌýFuel Cycle and Tritium Processing (FES, NE)
ÌýEÌýTritium Breeding Blankets (FES, NE)
ÌýFÌýFusion Plant Engineering and System Integration (FES)

6

Nuclear Restoration

ÌýAÌýEM AI R&D Roadmap Implementation (EM-3.2, ASCR, LM)
ÌýBÌýScale-Bridging AI Foundation Model (EM-3.2, ASCR)
ÌýCÌýTreatment Process Optimization (EM-3.2, ASCR)

7

Quantum Algorithms

ÌýAÌýApplication-aware Error Correction (ASCR)
ÌýBÌýComputational Tools for Fault Tolerant Quantum Computational Science (ASCR)
ÌýCÌýHybrid Quantum-Classical Optimization Algorithms (BES)
ÌýEÌýQuantum Advantage for Nuclear and Hadronic Systems (NP, HEP)

8

Quantum Systems

ÌýAÌýAI for Quantum Systems Design (BES)
ÌýDÌýAI for Quantum Computing and Networking (ASCR)

9

Microelectronics

ÌýAÌýAngstrom Scale Microelectronics Manufacturing (AMMTO)
ÌýCÌýAI-Driven Architecture Design (ASCR)
ÌýDÌý3D non-volatile compute-in-memory technology (ASCR)
ÌýFÌýMicroelectronics in Harsh Environments (HEP)
ÌýGÌýPlasma-Enabled Microelectronics Manufacturing (FES)
ÌýHÌýPower Electronics and Communication Networks (ASCR)
ÌýJÌýNeuromorphic Computing Connectivity and Integration (ASCR)

10

Data Centers

ÌýAÌýData Center Load Flexibility (ITO)
ÌýBÌýData Center Thermal Management (ITO)

11

Autonomous Labs

ÌýBÌýAIOps - AI for Network Operations (ASCR)
ÌýDÌýAI-Enabled Diagnostics and Remote Handling (FES)
ÌýEÌýNeuromorphic Computing for Robotic AI Systems (ASCR)

12

Materials Design

ÌýDÌýPlasma-Facing Materials (FES)
ÌýEÌýTargetry by Design (IRP)
ÌýGÌýElectrochemical Catalyst Discovery and Scale-up (AFFO)

14

Physics

ÌýBÌýAI Accelerated DUNE Science (HEP)

16

Grid Systems

ÌýAÌýGrid Modeling and Analysis (OE, CMEI-IESO, SC-ASCR)
ÌýCÌýUncertainty Quantification (SC-BER, SC-ASCR, OE, CMEI-IESO)

17

Subsurface Energy

ÌýAÌýChemical and Hydrologic Transport in Subsurface (BER)
ÌýCÌýControl of Subsurface Fractures (HGEO)

18

HPC AI

ÌýBÌýAutomated Scientific Problem-to-Code Generation (ASCR)
ÌýDÌýPerformance Prediction and Feedback Loops (ASCR)
ÌýEÌýTrustworthy AI for Scientific Software (ASCR)
ÌýFÌýMulti-Modal Data Integration for Code Intelligence (ASCR)
ÌýGÌýPartnerships for HPC AI Advancement (ASCR, AMMTO)

19

AI Reasoning

ÌýCÌýComposable and Modular Foundation Models (ASCR)

20

Cybersecurity AI

ÌýAÌýAI for Adversarial Robustness and Resilience (ASCR)
ÌýBÌýData Provenance and Integrity Verification (ASCR)
ÌýCÌýReal-Time Attack Detection and Mitigation for AI Models (ASCR)

21

Fluid Flow AI

ÌýBÌýAI-Driven Design and Control for Performance and Durability (IESO, ASCR)
ÌýCÌýData-Driven Operational Intelligence and System Resilience (IESO)

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