The Genesis Mission: Transforming Science and Energy with AI

The Genesis Mission: Transforming Science and Energy with AI is a federal funding opportunity issued by the U.S. Department of Energy through multiple offices including the Office of Science, Office of Critical Minerals and Energy Innovation, Office of Environmental Management, Office of Electricity, Hydrocarbons and Geothermal Energy Office, and Office of Nuclear Energy. This initiative is part of a broader national effort to accelerate scientific discovery and energy innovation using advanced artificial intelligence systems. The program is designed to support interdisciplinary teams that can develop and deploy AI-driven models and workflows to address critical challenges across energy, environmental, and national security domains. The primary purpose of this funding opportunity is to advance the Department of Energy mission by leveraging AI to improve research and development processes. Applicants are expected to propose innovative approaches that integrate AI into scientific workflows to achieve measurable improvements in predictive capabilities, experimental efficiency, and discovery timelines. The program supports a wide range of topic areas including advanced manufacturing, biotechnology, critical minerals, nuclear energy, quantum science, microelectronics, and environmental systems. Successful projects may contribute to broader DOE initiatives such as the American Science Cloud and the Transformational AI Models Consortium. Funding is structured across two phases. Phase I supports smaller teams with awards ranging from 500000 to 750000 dollars over a 9 month period, focused on demonstrating feasibility and measurable AI-driven improvements. Phase II supports larger teams and more advanced work, with funding envisioned at three to five times the Phase I award per year over a three year period. The total anticipated funding available for this opportunity is approximately 293760000 dollars, though the number of awards will depend on application quality and available appropriations. Funding is provided through the Department of Energy Other Transaction Authority, which may alter some administrative requirements. Eligibility for this opportunity is broad and includes domestic applicants such as institutions of higher education, nonprofit organizations, for-profit companies, and government-affiliated entities. Multi-institutional teams are required, with specific collaboration requirements depending on the phase. For example, Phase I requires partnerships across at least two categories such as national laboratories, industry, and academic or nonprofit institutions. Cost sharing is generally not required for research activities, except for for-profit entities, which must provide at least 20 percent cost share for research and up to 50 percent for demonstration activities. The application process includes multiple submission stages depending on the phase. For fiscal year 2026, Phase I applications and Phase II letters of intent are due by May 1, 2026, while full Phase II applications are due by May 19, 2026. Additional Phase II applications from Phase I awardees are due by December 17, 2026. Applicants must submit through Grants.gov and comply with requirements such as obtaining a Unique Entity Identifier and registering in the System for Award Management. Applications must include detailed project narratives, budget justifications, and supporting documentation as outlined in the solicitation. Applications are evaluated based on scientific and technical merit, relevance to program goals, team capability, and the potential for achieving transformative outcomes through AI integration. Additional program policy factors may include availability of computational resources and alignment with DOE priorities. The Department of Energy also provides opportunities for informational webinars and maintains a program contact email for applicant inquiries. The overall timeline includes application submission, review, selection, and award negotiation, with project periods beginning after selection and continuing through the defined phase durations.

Emphasize measurable AI advantage strong interdisciplinary teams and alignment with DOE mission priorities; demonstrate clear scalability from Phase I to Phase II