Berkeley Lab Computing Sciences

Did you know that Scientific Data Division researchers, in partnership with ATAP scientists, will lead the American Science Cloud (AmSC) Scientific User Facilities Infrastructure Partnership, which aims to develop the platform infrastructure to host and distribute AI models and scientific data to the broader research community?

“Our partnership is helping to address the unique computing challenges and exciting opportunities encountered by experimental scientists across the DOE’s user facilities...Collaborating across dozens of projects and seven national laboratories is both challenging and rewarding. The teamwork and the opportunity to learn from each other will make it a fun and enriching experience for everyone involved,” says Paolo Calafiura, a senior scientist in Berkeley Lab’s Scientific Data Division and co-lead on the project.

Read more: https://cs.lbl.gov/news-and-events/news/2026/harnessing-ai-for-particle-accelerator-innovation/

Learn more about how Berkeley Lab researchers are harnessing to accelerate discovery across particle accelerators, X-ray and neutron user facilities, biotechnology, and more, building on decades of foundational research and applied expertise.

"Through long-standing AI research, advanced computation, network facilities, and data integration, Berkeley Lab is supporting the U.S. Department of Energy’s Genesis Mission​, a national effort to address challenges in science, energy, and national security" — Jonathan Carter, Associate Laboratory Director, Berkeley Lab Computing Sciences

Berkeley Lab researchers are harnessing to accelerate discovery across particle accelerators, X-ray and neutron user facilities, biotechnology, and more, building on decades of foundational research and applied expertise.

(Details via first link in comments)

📌 OPAL, the Orchestrated Platform for Autonomous Laboratories to Accelerate AI-Driven BioDesign, is using robotic systems, AI agents and models, and standardized data-sharing platforms to accelerate the biotechnology pipeline, from gene discovery to commercialized technology.

📌 SYNAPS-I, Berkeley Lab’s new AI platform, transforms petabytes of imaging data from advanced light and neutron scattering facilities into discoveries across energy, microelectronics, medicine, and more.

📌 MOAT, the Multi-Office Particle Accelerator Team, led by Berkeley Lab, is adding AI to make particle accelerators even more impactful and to help revolutionize how we do science.

Learn more about the and projects from Lawrence Berkeley National Laboratory in the links in the comments below and stay tuned for our project-specific stories, rolling out all this week.

Argonne National Laboratory
Pacific Northwest National Laboratory
Oak Ridge National Laboratory

“Machine learning is game-changing for materials discovery because it saves scientists from repeating the same process over and over while testing new chemicals and making new materials in the lab,” said Berkeley Lab's Kristin Persson, the Materials Project Director and Co-Founder. “To be successful, machine learning programs need access to large amounts of high-quality, well-curated data. With its massive repository of curated data, the Materials Project is AI ready.”

What used to take months can now happen in days. Used more than 5,000 times a day, the Materials Project gives researchers immediate access to machine-learning ready materials data, helping speed up discovery for energy storage, quantum technologies, and advanced manufacturing.

“Accelerating materials discoveries is the key to unlocking new energy technologies.” — Anubhav Jain, Materials Project Associate Director (Link in first comment below)

NERSC
Berkeley Lab Energy Technologies Area

Can an AI generate an image that’s not just realistic, but scientifically true?
While anyone can generate a photorealistic cat, creating a valid microCT scan of a plant root that encodes real biology is a monumental challenge.
Berkeley Lab researchers have published one of the first in-depth evaluations comparing how different generative AI models tackle this very problem. Their findings are a crucial step toward a future where AI can bridge experimental gaps, reveal patterns too costly to find in the lab, and accelerate breakthroughs in materials science, biology, and energy research.
🔗Learn more: https://bit.ly/GenAI_LBNL
🧫 Read the full evaluation in the Journal of Imaging: https://www.mdpi.com/2313-433X/11/8/252

cc: U.S. Department of Energy