AI in Science: How LLNL is Accelerating Discovery with Machine Learning

Lawrence Livermore National Laboratory (LLNL) is pioneering the integration of artificial intelligence into scientific research, moving beyond simple automation to accelerate breakthrough discoveries. As featured on The Big Ideas Lab podcast, LLNL physicists Brian Spears and Kelli Humbird reveal how AI is revolutionizing their approach to complex scientific challenges.

Central to LLNL’s strategy is “cognitive simulation” (CogSim), which combines high-performance computing, experimental data, and AI models to rapidly generate and test scientific hypotheses. This approach proved instrumental in achieving fusion ignition in December 2022. By analyzing vast amounts of simulation data, AI successfully predicted which inertial confinement fusion designs had the highest probability of success—marking a historic breakthrough after six decades of research.

The applications extend far beyond fusion energy. LLNL’s AI platforms are accelerating drug discovery, potentially cutting years from treatment development timelines. Through collaborations with biotech companies and government entities, researchers have already developed new cancer drug candidates and made progress against evolving viruses like COVID-19.

Strategic partnerships with companies like OpenAI and Anthropic are bringing cutting-edge reasoning models into the Lab’s workflows, enhancing hypothesis generation and experimental design across multiple fields including advanced manufacturing and materials science.

However, as Humbird emphasizes, AI serves to augment rather than replace human expertise. These tools enhance efficiency and speed while maintaining the critical role of human judgment in scientific decision-making. LLNL’s integration demonstrates how combining human creativity with AI’s analytical power opens new possibilities for faster, smarter scientific experimentation.