Genesis Mission Phase II Application Template.xlsx
XLSX spreadsheet 34 KB Posted
- Opportunity number
- DE-FOA-0003612
- Issued by
-
Department of Energy
About this file
This is an application template for Phase II of the Genesis Mission, a DOE-sponsored initiative focused on accelerating scientific discovery and R&D workflows through artificial intelligence. The template is designed for large interdisciplinary teams applying for FY26 Phase II awards under the Office of Science Financial Assistance Program (CFDA 81.049). Eligible applicants include recipients of FY26 Phase I awards seeking to expand their teams and scope. The initiative spans nine topic areas: advanced manufacturing, biotechnology, critical materials, nuclear fission, nuclear fusion, quantum information science, semiconductors and microelectronics, discovery science, and energy. Teams are encouraged to leverage resources from DOE national laboratories, the National Nuclear Security Administration (NNSA), U.S. industry, and academia. Successful AI models and workflows may be integrated into the American Science Cloud to support broader scientific applications.
The Genesis Mission emphasizes addressing DOE's strategic priorities in energy, environmental management, and nuclear challenges through novel AI frameworks. Applications will undergo scientific merit review based on scientific and technical merit, appropriateness of methodology, competency of personnel and adequacy of resources, and budget reasonableness. DOE plans to amend the RFA within weeks to clarify LOI and application guidelines specific to Phase II awards. In FY27, DOE anticipates issuing amendments or alternative funding opportunities to support a second competition of both Phase I small team and Phase II large team applications. The Office of Science manages all applications through the Portfolio Analysis and Management System (PAMS) accessible at pamspublic.science.energy.gov.
Phase II Application Template
View the file
Other files
| File | Type | Posted |
|---|---|---|
| Sample-OT-and-Project-Agreements-for-DE-FOA-0003612 REV1.pdf | ||
| Sample OT and Project Agreements for DE-FOA-0003612 REV2.pdf | ||
| Sample OT and Project Agreements for DE-FOA-0003612.pdf | ||
| Genesis Mission Phase II LOI Template.xlsx | XLSX spreadsheet | |
| Genesis Mission Phase II LOI Template v2.xlsx | XLSX spreadsheet | |
| Genesis Mission Phase I Application Template.xlsx | XLSX spreadsheet | |
| Genesis Mission Phase I Application Template_v2.xlsx | XLSX spreadsheet | |
| Genesis Mission Phase I Application Template v2.xlsx | XLSX spreadsheet | |
| PKG00292132.pdf | ||
| DE-FOA-0003612.000002.pdf | ||
| DE-FOA-0003612.pdf | ||
| DE-FOA-0003612.000003.pdf | ||
| DE-FOA-0003612.000001.pdf |
Show all 13
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Text version
Phase II App Summary
| Application Stage | Phase II | |
| Focus Area - Primary | Select from dropdown menu | |
| Focus Area - Secondary 1 (optional) | ||
| Focus Area - Secondary 2 (optional) | ||
| Focus Area - Secondary 3 (optional) | ||
| Focus Area - Secondary 4 (optional) | ||
| Project Title | ||
| Total Budget | $ - 0 | Calculated using Budget Summary |
Key Investigators
| Key Investigator List | |||||||
| Instructions: Each senior/key personnel should be listed. Designeate the lead PI with an "X" in solumn A. For the Postiion, include a short description like "Professor", "Staff Scientist", or "Chief Engineer". | |||||||
| For Expertise, provide a short description of what key expertise the person brings to the proposed project. For example, "AI model developer" or "Theoretical chemist" | |||||||
| Lead PI | Last Name | First Name | Institution | Email address | Position | ORCID (optional) | Expertise |
Budget Summary
| Summary budget information for lead institution and all partner institutions | ||
| Institution | Year 1 | |
| ($) | Year 2 | |
| ($) | Year 3 | |
| ($) | Total |
| $ - 0 | ||||
| $ - 0 | ||||
| $ - 0 | ||||
| $ - 0 | ||||
| $ - 0 | ||||
| $ - 0 | ||||
| Total | $ - 0 | $ - 0 | $ - 0 | $ - 0 |
Compute Resource Estimate
| Compute Resource Estimate | * Note that this is not an allocation application. Information gathered here is for planning purposes only and does not gaurentee an allocation or serve as an allocation request. | ||||||||
| Instructions: | |||||||||
| Please enter your estimated computing needs and or allocations in "Computing Allocations or Needs" below. | |||||||||
| It is acceptable to leave blank cells if the requested information is not applicable. | |||||||||
| Column A (Allocation Program or Source): If you already have an available allocation or computing resource in persuit of this project, please state the allocation program or source here. Examples include: INCITE, ACCESS, Local compute cluster, AWS instance, ect. | |||||||||
| Column B (Machine): Please state the machine you anticipate requesting to use or have an existing allocation on if applicible. | |||||||||
| Column C (Estimated Compute Needs): Enter your expected compute needs. This should be a numerical, whole number. | |||||||||
| Column D (Compute Need Units): Units to accompany Column C. Acceptable units: GPU-hours, CPU-hours, node-hours, tokens | |||||||||
| Column E (Project Number): If a current DOE allocation will be utilized for this project, please list the project number. | |||||||||
| Column F (Expected data storage): If there is any data storage requirement, please enter it here. | |||||||||
| Computing Allocations or Needs: | |||||||||
| Allocation Program or Source (If existing allocation will be used) | Machine | Estimated Compute Need | Compute Need Units | Project Number | Expected data storage (TB) | Brief Code Description (500 character max) | Will the proposed project use or generate proprietary information, intellectual property, or restricted information? | Does this project use or generate code(s) or data that are subject to export control restrictions? | Additional Comments |
Focus Areas
| Topics |
| 1-A Reenvisioning Advanced Manufacturing and Industrial Productivity | Agentic AI-Driven Chemical Manufacturing |
| 1-B Reenvisioning Advanced Manufacturing and Industrial Productivity | AI-Driven Materials Processing |
| 1-C Reenvisioning Advanced Manufacturing and Industrial Productivity | AI-Enabled Manufacturing for Extreme Energy Systems |
| 1-D Reenvisioning Advanced Manufacturing and Industrial Productivity | Digitalization of Industrial Processes |
| 1-E Reenvisioning Advanced Manufacturing and Industrial Productivity | AI-Enabled Smart Manufacturing |
| 1-F Reenvisioning Advanced Manufacturing and Industrial Productivity | Energy Material Manufacturing |
| 2-A Scaling the Biotechnology Revolution | Biomolecular Science |
| 2-B Scaling the Biotechnology Revolution | Genotype to Phenotype |
| 2-C Scaling the Biotechnology Revolution | Predictive Engineering of Microbial Communities |
| 2-D Scaling the Biotechnology Revolution | Bio Design |
| 2-E Scaling the Biotechnology Revolution | AI-Enabled Biological Reaction Engineering, Bioreactor Design, Process Scale-up and Integration |
| 3-A Securing America’s Critical Minerals Supply | Resource Mapping and Development |
| 3-B Securing America’s Critical Minerals Supply | AI-Enabled Materials Discovery and Engineering |
| 3-C Securing America’s Critical Minerals Supply | Economic Modeling and Market Analysis |
| 3-D Securing America’s Critical Minerals Supply | Extraction and Processing Technologies |
| 3-E Securing America’s Critical Minerals Supply | Geological Finders/Keepers |
| 3-F Securing America’s Critical Minerals Supply | Connections for Isolation |
| 3-G Securing America’s Critical Minerals Supply | Biological Pathways to CMM |
| 4-A Delivering Nuclear Energy that is Faster, Safer, Cheaper | Accelerated Nuclear Power Plant Design and Licensing |
| 4-B Delivering Nuclear Energy that is Faster, Safer, Cheaper | Autonomous Power Plant Operations |
| 4-C Delivering Nuclear Energy that is Faster, Safer, Cheaper | AI-Assisted Manufacturing and Construction |
| 4-D Delivering Nuclear Energy that is Faster, Safer, Cheaper | Autonomous Research and Development |
| 4-E Delivering Nuclear Energy that is Faster, Safer, Cheaper | Accelerated Fuel Cycle Facility Design and Licensing to Secure the Domestic Fuel Supply |
| 4-F Delivering Nuclear Energy that is Faster, Safer, Cheaper | AI-Assisted Site Characterization |
| 4-G Delivering Nuclear Energy that is Faster, Safer, Cheaper | AI-Assisted End Disposition Design |
| 4-H Delivering Nuclear Energy that is Faster, Safer, Cheaper | Development, Utilization and/or Adoption of AI and ML Tools to Support the Efficient Review, Classification and Release of Legacy Documents to the Nuclear Industry |
| 5-A Accelerating Delivery of Fusion Energy | Structural Materials |
| 5-B Accelerating Delivery of Fusion Energy | Plasma-Facing Materials |
| 5-C Accelerating Delivery of Fusion Energy | Advancing Confinement Approaches |
| 5-D Accelerating Delivery of Fusion Energy | Fuel Cycle and Tritium Processing |
| 5-E Accelerating Delivery of Fusion Energy | Tritium Breeding Blankets |
| 5-F Accelerating Delivery of Fusion Energy | Fusion Plant Engineering and System Integration |
| 5-G Accelerating Delivery of Fusion Energy | Plasma Science and Technology |
| 6-A Transforming Nuclear Restoration and Revitalization | EM AI R&D Roadmap Implementation |
| 6-B Transforming Nuclear Restoration and Revitalization | Scale-Bridging AI Foundation Model |
| 6-C Transforming Nuclear Restoration and Revitalization | Treatment Process Optimization |
| 7-A Discovering Quantum Algorithms with AI | Application-aware Error Correction |
| 7-B Discovering Quantum Algorithms with AI | Computational Tools for Fault Tolerant Quantum Computational Science |
| 7-C Discovering Quantum Algorithms with AI | Hybrid Quantum-Classical Optimization Algorithms |
| 7-D Discovering Quantum Algorithms with AI | Quantum Algorithms for Nonlinear Plasma Physics |
| 7-E Discovering Quantum Algorithms with AI | Quantum Advantage for Nuclear and Hadronic Systems |
| 8-A Realizing Quantum Systems for Discovery | AI for Quantum Systems Design |
| 8-B Realizing Quantum Systems for Discovery | AI for Control of Quantum System |
| 8-C Realizing Quantum Systems for Discovery | AI for Quantum Imaging and Sensing |
| 8-D Realizing Quantum Systems for Discovery | AI for Quantum Computing and Networking |
| 9-A Recentering Microelectronics in America | Angstrom (sub-1-nm) Scale Microelectronics Manufacturing |
| 9-B Recentering Microelectronics in America | Materials and Architectures for Non-von Neuman Computing Devices |
| 9-C Recentering Microelectronics in America | AI-Driven Architecture Design |
| 9-D Recentering Microelectronics in America | 3D non-volatile compute-in-memory technology |
| 9-E Recentering Microelectronics in America | Physics-Based Circuit Design, Simulation, and Emulation |
| 9-F Recentering Microelectronics in America | Microelectronics in Harsh Environments |
| 9-G Recentering Microelectronics in America | Plasma-Enabled Microelectronics Manufacturing |
| 9-H Recentering Microelectronics in America | Power Electronics and Communication Networks |
| 9-I Recentering Microelectronics in America | Low-temperature Electronics for Sensors and Computation |
| 9-J Recentering Microelectronics in America | Transform Neuromorphic Computing Connectivity, Communication, and System Hardware Integration |
| 10-A Securing U.S. Leadership in Data Centers | Data Center Load Flexibility |
| 10-B Securing U.S. Leadership in Data Centers | Data Center Thermal Management |
| 11-A Achieving AI-Driven Autonomous Laboratories | Advanced Robotics for Dynamic Laboratory Environments |
| 11-B Achieving AI-Driven Autonomous Laboratories | AIOps - AI for Network Operations |
| 11-C Achieving AI-Driven Autonomous Laboratories | AI-Accelerated Science: Correlation to Understanding |
| 11-D Achieving AI-Driven Autonomous Laboratories | AI-Enabled Diagnostics and Remote Handling |
| 11-E Achieving AI-Driven Autonomous Laboratories | Accelerate the design and prototyping of neuromorphic computing circuit primitives for robotic embodied physical artificial intelligence |
| 12-A Designing Materials with Predictable Functionality | Functional to Quantum Materials |
| 12-B Designing Materials with Predictable Functionality | Structural Materials |
| 12-C Designing Materials with Predictable Functionality | Biomolecular Materials |
| 12-D Designing Materials with Predictable Functionality | Plasma-Facing Materials |
| 12-F Designing Materials with Predictable Functionality | AI-Enabled Materials Discovery, Development, and Qualification |
| 12-G Designing Materials with Predictable Functionality | Electrochemical Energy Conversion Catalyst Discovery and Scale up |
| 13-A Enhancing Particle Accelerators for Discovery | AI-driven Accelerator Facilities |
| 13-B Enhancing Particle Accelerators for Discovery | Integration of Digital Twins for Fusion Systems and Actuators |
| 14-A Unifying Physics from Quarks to the Cosmos | Foundation Models of Particle Interactions and Cosmic Physics |
| 14-B Unifying Physics from Quarks to the Cosmos | AI Accelerated DUNE Science |
| 14-C Unifying Physics from Quarks to the Cosmos | Expedited Discovery from High Complexity and Petabyte-Scale Datasets |
| 15-A Predicting U.S. Water for Energy | Cloud Microphysics and Atmospheric Turbulence |
| 15-B Predicting U.S. Water for Energy | Water and Energy |
| 15-C Predicting U.S. Water for Energy | Weeks to Years Prediction |
| 16-A Scaling the Grid to Power the American Economy | Grid Modeling and Analysis |
| 16-B Scaling the Grid to Power the American Economy | Grid Operations Optimization |
| 16-C Scaling the Grid to Power the American Economy | Uncertainty Quantification |
| 17-A Unleashing Subsurface Strategic Energy Assets | Chemical and Hydrologic Transport in Subsurface |
| 17-B Unleashing Subsurface Strategic Energy Assets | Evolution of Fractures in the Upper Crust |
| 17-C Unleashing Subsurface Strategic Energy Assets | Control of Subsurface Fractures |
| 18-A HPC Code Curation, Translation, and Development for Accelerated Scientific Discoveries | AI-Driven Code Porting and Optimization |
| 18-B HPC Code Curation, Translation, and Development for Accelerated Scientific Discoveries | Automated Scientific Problem-to-Code Generation |
| 18-C HPC Code Curation, Translation, and Development for Accelerated Scientific Discoveries | Neuro-Symbolic Agents for Code Development |
| 18-D HPC Code Curation, Translation, and Development for Accelerated Scientific Discoveries | Performance Prediction and Feedback Loops |
| 18-E HPC Code Curation, Translation, and Development for Accelerated Scientific Discoveries | Trustworthy AI for Scientific Software |
| 18-F HPC Code Curation, Translation, and Development for Accelerated Scientific Discoveries | Multi-Modal Data Integration for Code Intelligence |
| 18-G HPC Code Curation, Translation, and Development for Accelerated Scientific Discoveries | Partnerships for HPC AI Advancement |
| 19-A AI for Scientific Reasoning | Trustworthy Mathematical and Symbolic Reasoning |
| 19-B AI for Scientific Reasoning | Hypothesis Generation from Multi-Modal Data |
| 19-C AI for Scientific Reasoning | Composable and Modular Foundation Models |
| 20-A Cybersecurity for AI-driven Science Workflows | AI for Adversarial Robustness and Resilience |
| 20-B Cybersecurity for AI-driven Science Workflows | Data Provenance and Integrity Verification |
| 20-C Cybersecurity for AI-driven Science Workflows | Real-Time Attack Detection and Mitigation for AI Models |
| 21-A Artificial Intelligence in Fluid Flow for Energy Components and Technologies | Physics-Informed AI for Complex Flow Modeling |
| 21-B Artificial Intelligence in Fluid Flow for Energy Components and Technologies | AI-Driven Design and Control for Performance and Durability |
| 21-C Artificial Intelligence in Fluid Flow for Energy Components and Technologies | Data-Driven Operational Intelligence and System Resilience |
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