Funding Opportunity: DOE Genesis Mission ($293M) for Transforming Science and Energy with AI

The U.S. Department of Energy (DOE) has announced a new funding opportunity focused on advancing AI applications in scientific discovery and energy systems. Here is the information from NIH:

"Earlier this week The U.S. Department of Energy (DOE) announced significant funding to support the Genesis Mission. This includes a $293 million Request for Application (RFA), titled “The Genesis Mission: Transforming Science and Energy with AI”: https://science.osti.gov/-/media/grants/pdf/foas/2026/DE-FOA-0003612.pdf.

The RFA is open to teams from DOE National Laboratories, U.S. industry, and academia. Phase I awards will range from $500,000 to $750,000 for a nine-month project period, while Phase II awards will range from $6 million to $15 million over a three-year project period. Teams can apply directly to either phase in FY 2026, with successful Phase I teams eligible for larger Phase II awards in future cycles.

Key dates:

- Phase I applications and Phase II letters of intent are due April 28, 2026.

- Phase II applications are due May 19, 2026.

- An informational webinar about this RFA will be held on March 26, 2026: https://science-doe.zoomgov.com/webinar/register/WN_cByyhWASR72Do7yIDpe3_g 

There are several Challenge areas in the RFA, noted below, that align with the NIH mission. Please share this RFA widely with your research communities.

• # 2-Scaling the Biotech Revolution: connecting protein structure w/function, genotype to phenotype, bio design/engineering
• #7-Discovering Quantum Algorithms with AI: error correction/fault tolerance, hybrid systems, AI + quantum for science/computation
• #11-Achieving AI-Driven Autonomous Labs: robotics, research network ops, hypothesis generation, neuromorphic computing for robots
• #18-HPC Code Curation, Translation, and Dev for Accelerated Scientific Discoveries: automated code porting/optimization, trustworthy AI for software, multimodal data for training foundation models
• #19-AI for Scientific Reasoning: foundation models for scientific theories,  hypothesis generation from multimodal data, modular foundation models
• #20-Cybersecurity for AI-driven Science Workflows: model attack mitigation/response, data provenance/integrity"

Source and more information: 

1. NIH email

2. https://science.osti.gov/-/media/grants/pdf/foas/2026/DE-FOA-0003612.pdf