Scientists and engineers from Lawrence Livermore National Laboratory were chosen to lead 10 projects under the U.S. Department of Energy’s first phase of the Genesis Mission, an initiative utilizing artificial intelligence to advance scientific discovery.
At the Genesis Mission summit in Washington, D.C, DOE awarded 278 projects across nearly 350 institutions, including DOE laboratories, universities, private organizations, and nonprofits. LLNL researchers will contribute to 19 additional projects as part of partner-led collaborations. Also contributing to Genesis Mission are major Bay Area labs Lawrence Berkeley National Laboratory and SLAC National Accelerator Laboratory in Stanford.
“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.”
Genesis Mission, according to DOE’s website, harnesses AI for “breakthroughs in energy dominance, discovery science, and national security,” seeking “to build the world’s most powerful scientific platform”.
According to LLNL, it 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.
DOE’s phase one funding awards aim to narrow down “promising pathways towards transformative scientific capabilities” through AI-powered innovation and experimentation, according to a press release from LLNL’s Office of Strategic Communications. LLNL researchers will lead projects applying AI to “high-performance computing (HPC), fusion energy, Earth systems science, materials discovery, biology, quantum technologies and fundamental physics”.
Visit here for more information on LLNL-led projects and partner-led collaborations involving the lab.
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