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 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 | ÌýA | ÌýAgentic AI-Driven Chemical Manufacturing (BES) |
|---|---|---|---|
| ÌýC | ÌýAI-Enabled Manufacturing for Extreme Energy Systems (FES) | ||
| ÌýD | ÌýDigitalization of Industrial Processes (ITO) | ||
| ÌýF | ÌýEnergy Material Manufacturing (AFFO) | ||
2 | Biotechnology | C | Predictive Engineering of Microbial Communities (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 | ||
| ÌýE | ÌýAccelerated Fuel Cycle Facility Design and Licensing to Secure the Domestic Fuel Supply | ||
| 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) | ||
| ÌýH | ÌýPower Electronics and Communication Networks (ASCR) | ||
| 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) |
| 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 | ÌýTargetry by Design (IRP) | ||
| ÌýG | ÌýElectrochemical Catalyst Discovery and Scale-up (AFFO) | ||
14 | Physics | ÌýB | ÌýAI Accelerated DUNE Science (HEP) |
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) |
| ÌýD | ÌýPerformance Prediction and Feedback Loops (ASCR) | ||
| ÌýE | ÌýTrustworthy AI for Scientific Software (ASCR) | ||
| ÌýF | ÌýMulti-Modal Data Integration for Code Intelligence (ASCR) | ||
| ÌýG | ÌýPartnerships for HPC AI Advancement (ASCR, AMMTO) | ||
19 | AI Reasoning | ÌýC | ÌýComposable and Modular Foundation Models (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) |
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