DOE Genesis Mission: Transforming Science and Energy with AI
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 must email ltdsubs@colorado.edu by 12pm MT on April 17, 2026. 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 | ÌýA | ÌýAgentic AI-Driven Chemical Manufacturing (BES) |
|---|---|---|---|
| ÌýB | AI-Driven Materials Processing (BES) | ||
| ÌýC | ÌýAI-Enabled Manufacturing for Extreme Energy Systems (FES) | ||
| ÌýD | ÌýDigitalization of Industrial Processes (ITO) | ||
| ÌýE | AI-Enabled Smart Manufacturing (AMMTO) | ||
| ÌýF | ÌýEnergy Material Manufacturing (AFFO) | ||
2 | Biotechnology | ÌýE | AI-Enabled Biological Reaction Engineering, Bioreactor Design, Process Scale-up and Integration (AFFO) |
| Ìý3 | Critical Minerals | ÌýB | AI-Enabled Materials Discovery and Engineering (AMMTO) |
| ÌýC | Economic Modeling and Market Analysis (ASO) | ||
| ÌýG | Biological Pathways to CMM (BER) | ||
4 | Nuclear Energy | ÌýA | ÌýAccelerated Nuclear Power Plant Design and Licensing |
| ÌýB | ÌýAutonomous Power Plant Operations | ||
| ÌýC | ÌýAI-Assisted Manufacturing and Construction | ||
| ÌýD | ÌýAutonomous Research and Development | ||
| ÌýF | ÌýAI-Assisted Site Characterization | ||
| ÌýG | ÌýAI-Assisted End Disposition Design | ||
| ÌýH | ÌýAI/ML Tools for Review and Release of Legacy Documents | ||
5 | Fusion Energy | ÌýA | ÌýStructural Materials (FES) |
| ÌýB | ÌýPlasma-Facing Materials (FES) | ||
| ÌýC | ÌýAdvancing Confinement Approaches (FES) | ||
| ÌýD | ÌýFuel Cycle and Tritium Processing (FES, NE) | ||
| ÌýE | ÌýTritium Breeding Blankets (FES, NE) | ||
| ÌýF | ÌýFusion Plant Engineering and System Integration (FES) | ||
6 | Nuclear Restoration | ÌýA | ÌýEM AI R&D Roadmap Implementation (EM-3.2, ASCR, LM) |
| ÌýB | ÌýScale-Bridging AI Foundation Model (EM-3.2, ASCR) | ||
| ÌýC | ÌýTreatment Process Optimization (EM-3.2, ASCR) | ||
7 | Quantum Algorithms | ÌýA | ÌýApplication-aware Error Correction (ASCR) |
| ÌýB | ÌýComputational Tools for Fault Tolerant Quantum Computational Science (ASCR) | ||
| ÌýC | ÌýHybrid Quantum-Classical Optimization Algorithms (BES) | ||
| ÌýE | ÌýQuantum Advantage for Nuclear and Hadronic Systems (NP, HEP) | ||
8 | Quantum Systems | ÌýA | ÌýAI for Quantum Systems Design (BES) |
| ÌýD | ÌýAI for Quantum Computing and Networking (ASCR) | ||
9 | Microelectronics | ÌýA | ÌýAngstrom Scale Microelectronics Manufacturing (AMMTO) |
| ÌýC | ÌýAI-Driven Architecture Design (ASCR) | ||
| ÌýD | Ìý3D non-volatile compute-in-memory technology (ASCR) | ||
| ÌýF | ÌýMicroelectronics in Harsh Environments (HEP) | ||
| ÌýG | ÌýPlasma-Enabled Microelectronics Manufacturing (FES) | ||
| ÌýJ | ÌýNeuromorphic Computing Connectivity and Integration (ASCR) | ||
10 | Data Centers | ÌýA | ÌýData Center Load Flexibility (ITO) |
| ÌýB | ÌýData Center Thermal Management (ITO) | ||
11 | Autonomous Labs | ÌýB | ÌýAIOps - AI for Network Operations (ASCR) |
| ÌýC | AI-Accelerated Science: Correlation to Understanding (BES) | ||
| ÌýD | ÌýAI-Enabled Diagnostics and Remote Handling (FES) | ||
| ÌýE | ÌýNeuromorphic Computing for Robotic AI Systems (ASCR) | ||
12 | Materials Design | ÌýD | ÌýPlasma-Facing Materials (FES) |
| ÌýE | Targetry by Design (IRP) | ||
| ÌýG | ÌýElectrochemical Catalyst Discovery and Scale-up (AFFO) | ||
14 | Physics | ÌýA | Foundation Models of Particle Interactions and Cosmic Physics (HEP, NP) |
| ÌýB | ÌýAI Accelerated DUNE Science (HEP) | ||
| 15 | Water Systems | ÌýA | Cloud Microphysics and Atmospheric Turbulence (BER, IESO) |
| ÌýB | Water and Energy (BER) | ||
| ÌýC | Weeks to Years Prediction (BER) | ||
16 | Grid Systems | ÌýA | ÌýGrid Modeling and Analysis (OE, CMEI-IESO, SC-ASCR) |
| ÌýC | ÌýUncertainty Quantification (SC-BER, SC-ASCR, OE, CMEI-IESO) | ||
17 | Subsurface Energy | ÌýA | ÌýChemical and Hydrologic Transport in Subsurface (BER) |
| ÌýC | ÌýControl of Subsurface Fractures (HGEO) | ||
18 | HPC AI | ÌýB | ÌýAutomated Scientific Problem-to-Code Generation (ASCR) |
| ÌýC | Neuro-Symbolic Agents for Code Development (ASCR) | ||
| ÌýD | ÌýPerformance Prediction and Feedback Loops (ASCR) | ||
| ÌýF | ÌýMulti-Modal Data Integration for Code Intelligence (ASCR) | ||
| ÌýG | ÌýPartnerships for HPC AI Advancement (ASCR, AMMTO) | ||
19 | AI Reasoning | ÌýA | Trustworthy Mathematical and Symbolic Reasoning (ASCR) |
20 | Cybersecurity AI | ÌýA | ÌýAI for Adversarial Robustness and Resilience (ASCR) |
| ÌýB | ÌýData Provenance and Integrity Verification (ASCR) | ||
| ÌýC | ÌýReal-Time Attack Detection and Mitigation for AI Models (ASCR) | ||
21 | Fluid Flow AI | ÌýB | ÌýAI-Driven Design and Control for Performance and Durability (IESO, ASCR) |
| ÌýC | ÌýData-Driven Operational Intelligence and System Resilience (IESO) |
Ìý