Topic: AI/ML

By combining causal-inference methods with machine learning, researchers evaluated 162 medications to identify drugs prescribed for other conditions that were associated with meaningful differences in survival.

Six LLNL Computing researchers have been named Distinguished Members of Technical Staff in recognition of their extraordinary scientific and technical contributions.

To learn more about the work taking place at Livermore Computing and the potential this has for a wide range of real-world applications, The Innovation Platform spoke to LLNL’s Deputy for High Performance Computing, Judy Hill.

In a pioneering partnership to accelerate materials discovery with AI, researchers from LLNL and Meta have created the world’s largest open dataset of atomistic polymer chemistry.

An LLNL-led team is using 700,000 node-hours of Department of Energy HPC resources to improve developer productivity.

Our researchers will be well represented at the SIAM Conference on Parallel Processing for Scientific Computing (PP26) on March 3–6. SIAM is the Society for Industrial and Applied Mathematics with an international community of more than 14,000 individual members.

LLNL, in collaboration with the California Foundation for Commerce and Livermore Lab Foundation, has released a new report to help California lawmakers navigate the fast-moving AI landscape.

During the weeklong conference, attendees visiting the Department of Energy’s booth were treated to two technical demonstrations and a talk by LLNL staff.

A sophisticated, cost-effective framework combines HPC, ML models, and mathematical algorithms to optimize power grid stability and security.

LLNL’s presence, which included dozens of sessions, including tutorials, workshops, paper presentations and birds-of-a-feather meetings was felt across virtually every major event of the week.

How do we keep artificial intelligence safe and secure as it advances at breakneck speed? This video explores the risks of AI systems powering today’s chatbots, virtual assistants, and more.

Join LLNL at the 39th annual Conference on Neural Information Processing Systems on December 2–7.

Livermore researchers are engaged in efforts to apply correctness and formal methods to improve the reliability, reproducibility, and accuracy of the Laboratory’s high performance computing codes.

Scientists at LLNL and collaborators at AMD and Columbia University have achieved a milestone in biological computing: completing the largest and fastest protein structure prediction workflow ever run, using the full power of El Capitan.

LLNL is participating in the 37th annual Supercomputing Conference (SC25) in St. Louis on November 16–21, 2025.

A new study led by CASC researchers empowers users to interact with the application through natural-language and visual inputs instead of the typical graphic user interface, which can appear daunting for novice users.

Building on our leadership in HPC and AI and our long open-source tradition, ElMerFold is a high performance framework for large-scale inference and distillation on LLNL supercomputers with OpenFold-specific optimizations.

LLNL researchers employed an AI-driven model to predict fusion ignition days ahead of the historic 2022 shot, according to a new study in Science.

Part of an AI framework called the Multi-Agent Design Assistant (MADA), LLNL scientists and collaborators are merging LLMs with simulation tools to interpret natural language prompts and using the platform to generate full physics simulation decks for LLNL’s MARBL multiphysics code.

As Computing’s ninth Fernbach Fellow, postdoctoral researcher Daniel Nichols will explore how AI can accelerate HPC and computational science under the mentorship of Harshitha Menon.

A new CASC paper proposes unity and clarity around foundation models in computational science, offering an implementation framework inspired by finite element methods.

LLNL researchers have posters and workshop papers accepted to the 42nd International Conference on Machine Learning on July 13–19.

A new cancer drug candidate developed by LLNL, BridgeBio Oncology Therapeutics, and the Frederick National Laboratory for Cancer Research has demonstrated the ability to block tumor growth without triggering a common and debilitating side effect.

Organizers praised Falcone's “extraordinary leadership in driving research directions to advance science and technology at one of America's most vital research institutions.”

As the application of AI across industries accelerates the pace of development, so too must national security remain at the cutting edge, a task requiring extensive collaboration to deploy the nation’s most critical resources.