Recently, the University of California (UC), Los Alamos National Laboratory (LANL) and Lawrence Livermore National Laboratory (LLNL) convened researchers and leaders from across the UC system in Santa Fe, New Mexico, for the first annual AI Science at Scale summit. The summit was planned in recognition of AI's growing role in scientific discovery and the Department of Energy's increasing interest in frontier AI, which subsequently formalized in the Genesis mission.
Backed by a $19 million investment from fee income UC earned from managing its DOE national labs, the AI Science at Scale initiative awarded grants to UC and UC-affiliated national laboratory researchers at LLNL and LANL, reinvesting in the DOE scientific enterprise. From a competitive pool of researchers from across the UC system, four multi-campus teams were selected in April 2025, supporting research in AI-driven genomics, quantum materials discovery, geothermal energy, and integrated data platforms.
"We launched the AI Science at Scale initiative because we saw that artificial intelligence is becoming a foundational capability for scientific discovery and national security missions," said June Yu, Vice President for UC National Laboratories. "By strategically reinvesting University management fee income, we chose to accelerate collaboration across UC and our national laboratories, build enduring research partnerships, and develop the talent needed to apply AI to some of the nation's most complex scientific challenges."
The four selected project teams, led by principal investigators from UC San Francisco, UC Santa Barbara, UC Irvine, and UC San Diego, are not merely researching AI; they are building the foundational architecture for the future of American scientific competitiveness. By training sequence-to-function models, leveraging AI foundation models to predict subsurface physics and advance materials discovery, and advancing integrated AI-relevant data ecosystems, the teams are operating at the bleeding edge of discovery.
"This initiative highlights the power of partnership between UC and the national laboratories," added Eric Schwegler, who leads the Academic Engagement Office & Science Education at LLNL. "By connecting UC's research strengths with LLNL's high-performance computing, AI, and mission-driven science expertise, we can accelerate discovery in critical areas and help prepare the next generation of scientists and engineers for the DOE mission."
"AI at scale demands capabilities no single institution possesses on its own: data, computation and domain expertise," said Ruben Glatt, director of the Center for Advanced Signal and Image Science. "The AI Science at Scale program succeeds precisely because it allows each cross-disciplinary team to contribute its particular strength, transforming individual efforts into genuine collaboration. Bringing the teams together to showcase their progress, observe one another's work and discuss course corrections adds real value to the scientific process."
