Insider Brief
- The Department of Energy announced $159 million for 12 new Phase II Genesis Mission projects using AI and advanced computing to accelerate scientific research.
- The projects include Harvard -led quantum computing error correction, Oak Ridge-led quantum magnet design, and efforts in fusion energy, biotechnology, geothermal energy and particle physics.
- The selections bring the mission’s Phase II total to 14 projects, while six additional Phase I awards supplement the earlier funding round.
PRESS RELEASE — The U.S. Department of Energy (DOE) today announced 12 Phase II project awards for President Trump’s historic Genesis Mission, totaling $159 million, that will expand efforts to harness artificial intelligence (AI), advanced computing, and the nation’s world-class scientific capabilities to accelerate the pace of discovery.
“These projects represent a critical step in turning the promise of AI for science into transformative scientific capability,” said DOE Under Secretary for Science Dr. Darío Gil. “The Phase II teams have already demonstrated what is possible when AI is integrated with scientific expertise, data, and computing, and these awards give them the opportunity to take that work to the next level. By bringing together our National Labs, universities, and industry partners, we are building on extraordinary work already underway and accelerating the pace of discovery.”
The awards build on the Genesis Mission’s growing portfolio of projects, first announced in July, to address critical national science and technology challenges . By connecting America’s leading scientists with advanced AI, supercomputing, scientific data and DOE’s unique research infrastructure, the Genesis Mission is creating new capabilities to accelerate scientific discovery and expand the frontiers of U.S. science and technology.
Awarded Projects
Through the Office of Science, the 12 newly awarded projects are:
- Accelerating the path to commercial fusion energy via an AI-enabled digital twin platform for SPARC: Led by Commonwealth Fusion Systems, this project will build a digital twin to simulate and optimize operations of a fusion demonstration device.
- Decoding the RNA Structurome to Secure AI Advantage for the Bioeconomy: Led by the University of California San Diego , the project will expand the RNA structure database five-fold for training AI models to unlock the ability to engineer resilient crops and sustainable microbes.
- Lattice Quantum Chromodynamics (LQCD) at the Intelligence Frontier: Led by MIT , this project will integrate artificial intelligence and supercomputing calculations of fundamental particle physics to dramatically accelerate discoveries about the basic building blocks of our universe.
- Multi-agent AI Expert for Subsurface Reasoning and Optimization (MAESTRO): Led by the University of California Irvine , MAESTRO will establish an AI platform to accurately map, stimulate, and manage deep underground reservoirs to unleash vast resources of reliable geothermal energy.
- AI-extended Interfacial Separations (AXIS): A Multiscale Framework for Redox-Active Ligand Design: Led by the University of Illinois Urbana-Champaign , AXIS will harness AI to design more efficient electrochemical processes for extracting critical rare-earth elements from domestic sources.
- Overcoming barriers in computational enzyme design through improved sequence-structure ensemble modeling: Led by the University of Washington Seattle , this project will build foundational AI tools to predict and design functional enzymes.
- Accelerating Extreme Environment Specs-to-Silicon (AXESS): Led by Fermilab, AXESS will use AI to rapidly design rugged, high-performance microchips capable of operating in extreme conditions such as space, high radiation, and ultra-cold environments.
- The Multi-office Accelerator Team Core (MOAT-Core): Led by Lawrence Berkeley National Laboratory , MOAT-Core will develop a shared AI platform across National Laboratories to autonomously run and design particle accelerators faster and more efficiently.
- Application-Aware Error Correcting Codesign for Scientific Quantum Computing (ASQC): Led by Harvard University , ASQC combines AI with advanced quantum hardware to tackle quantum computing error correction.
- An Iterative Framework of AI-Assisted Scientific Development and Optimization (AI4HPC): Led by Argonne National Laboratory , AI4HPC will create an AI framework to modernize, optimize, and verify supercomputing scientific software.
- AI-empowered Design of Functional Quantum Magnets: Led by Oak Ridge National Laboratory , this project will develop a physics-informed AI framework to design new quantum magnets by working backward from desired properties, with applications in quantum sensing, low-power electronics, and quantum technologies.
- Agentic Assistance for Advanced Rare Isotope Separator Operations at the Facility for Rare Isotope Beams (FRIB): Led by Northeastern University , this project will develop an intelligent, automated assistant to safely streamline the operation and tuning of the FRIB particle separator, unlocking hundreds of hours of research time annually.
These new selections bring the Phase II total to 14, alongside previously announced awards for GridFM 2.0 under the Office of Electricity and Prometheus under the Office of Nuclear Energy.
Additionally, six new Phase I awards are being issued, supplementing the earlier round of selections.
More information about these and the previously awarded projects is available here .