Lawrence Livermore National Laboratory (LLNL) scientists and engineers have been selected to lead 10 Phase I projects under the U.S. Department of Energy's (DOE's) Genesis Mission, applying AI to challenges spanning high-performance computing (HPC), fusion energy, Earth systems science, materials discovery, biology, quantum technologies and fundamental physics.
LLNL researchers also will contribute to 19 additional Genesis Mission projects led by partner institutions across the national laboratories, academia and industry. DOE announced the awardees July 22 at the Genesis Mission Summit in Washington, D.C.
"These selections reflect LLNL's demonstrated ability to integrate AI, advanced computing, experimental science and multidisciplinary expertise to solve problems of national importance," LLNL Director Kim Budil said. "The Genesis Mission provides an opportunity to take on a remarkable range of scientific challenges and help redefine the pace at which research can move from new ideas to real-world impact."
The Genesis Mission is a DOE-led national initiative to build the world's most powerful integrated AI-enabled science discovery platform. By uniting government, industry, academia and philanthropy, it aims to accelerate breakthroughs in energy, science and national security.
The Phase I awards aim to identify promising pathways toward transformative scientific capabilities and future investment. Teams will demonstrate workflows integrating AI with scientific investigation and evaluate their potential to accelerate discovery, improve prediction and enhance experimentation.
LLNL will lead projects to:
- Expand multimodal AI for nuclear and particle physics, integrating data from complementary particle detectors to accelerate scientific insight.
- Develop digital twins for laser-plasma acceleration, linking plasma-channel formation, beam dynamics and AI-enabled optimization.
- Advance AI-enabled materials discovery by predicting defects and material properties for next-generation computing.
- Improve Earth system prediction through scalable AI approaches to turbulence, cloud processes and high-resolution atmospheric modeling.
- Develop agentic AI for experimental science, including portable laser diagnostics and interoperable integration across LaserNetUS facilities.
- Accelerate precision manufacturing for fusion, using AI-enabled data pipelines for additive manufacturing of high-precision targets.
- Speed cosmological inference from Rubin Observatory data through fast, uncertainty-aware AI methods.
- Advance AI for HPC by characterizing scientific workload performance directly from binary executables.
- Design new metalloproteins through biophysics-informed AI approaches that learn and predict metal coordination.
- Advance superconducting microsystems and quantum control through physics-reinforced AI.
LLNL researchers also will participate in partner-led projects spanning advanced computing, autonomous laboratories, materials, fusion, Earth systems, biology and fundamental physics.
