The Genesis Mission: Transforming Science and Energy with AI (FES)

Genesis Mission AI FOA — Phase I and Phase II (FES)

Supports AI-enabled fusion and plasma research with continuation pathways for strong performers.

FOA DE-FOA-0003612, titled "The Genesis Mission: Transforming Science and Energy with AI," is a DOE Office of Science solicitation posted March 17, 2026 and updated April 20, 2026, covering artificial intelligence applications across all Office of Science program areas including Fusion Energy Sciences (FES). Under the FES portfolio, this FOA targets AI and machine learning applications for fusion science and plasma physics research. The solicitation's total funding envelope is $293 million across the Genesis Mission program spanning 26 national science and technology challenges. FES-relevant challenges include AI-driven autonomous laboratories, designing materials with predictable functionality, and scaling scientific computing capacity.

The FOA is structured in two phases. Phase I awards ranged from $500,000 to $750,000 for nine-month projects; Phase II awards ranged from $6 million to $15 million over three years. Phase I applications and Phase II letters of intent for new applicants were due May 1, 2026; Phase II applications from new applicants closed May 19, 2026. The window that remains live as of June 2026 is the Phase II continuation track for FY2026 Phase I awardees, with a deadline of December 17, 2026. Applications are submitted through Grants.gov; Phase II letters of intent go through PAMS. Eligible applicants include DOE national laboratory teams, U.S. industry, and academia; individuals may not apply.

FES researchers and fusion companies that received a Phase I award under the FY2026 Genesis Mission cycle should prioritize preparing Phase II applications before the December 17, 2026 deadline. New entrants to the Genesis Mission program should monitor DOE's Office of Science funding portal (science.osti.gov/Funding-Opportunities) for the FY2027 cycle. Strong applications in the fusion AI space will likely emphasize machine learning for plasma control, predictive modeling of disruptions, and AI-accelerated materials discovery for fusion reactor components.