What is the American Science and Security Cloud? Data Platforms Program Lead Dylan McReynolds explains how the ALS is using this network of tools and helping build an even more robust platform. Read more »
A World of Vibe Coding Opportunities at the ALS
In April 2026, a panel of vibe coders hosted a tutorial at the ALS. From a general overview to specific strategies to optimize the code, participants learned all about how vibe coding can improve their workflows. Read more »
Accelerate UX Workshop Brings Global Expertise Together at the ALS
Staff from eleven different accelerator lab facilities gathered at the ALS to improve the user experience for operators, researchers, engineers, and more. Workshop participants learned from experts in the UX field and even took part in a hackathon that paired beamline scientists with interface developers. Read more »
AI for Smarter, More Powerful, More Efficient Particle Accelerators
The Multi-Office particle Accelerator Team (MOAT) is developing artificial intelligence tools to improve particle accelerators and speed breakthroughs. The collaborative effort is led by Berkeley Lab and is part of the Genesis Mission, a new national AI initiative. Read more »
How a Machine Learning Pipeline Could Accelerate Innovation
SYNAPS-I, a new multi-lab AI platform supporting DOE’s Genesis Mission, aims to accelerate discoveries at advanced light and neutron scattering user facilities. The results could speed breakthroughs in energy, semiconductors, medicine, and many other technologies critical to modern society. Read more »
AI Delivers Rapid, Precise Design of Tumor-Targeting Protein
A new protein designed using AI can precisely recognize a key therapeutic target for cancer. X-ray crystallography data collected at the ALS confirmed the new protein’s specificity for its target, demonstrating a configurable and scalable approach to cancer therapy. Read more »
Berkeley Lab Hosts Agentic AI for User Facilities Workshop
Berkeley Lab hosted a workshop on Agentic AI for User Facilities with about 100 registered participants from user facilities across the US national lab complex and European light sources. The two main goals of the workshop were to identify cross-facility patterns, gaps, and design principles for agentic AI at DOE user facilities, and ground agentic AI in domain realities and identify domain-specific constraints, opportunities, and readiness. Read more »
Robotics Project Pushes Toward Self-Driving Materials Optimization
A new multi-disciplinary team aims to automate complex sample handling at Beamline 7.3.3, leveraging AI and robotics to speed up material optimization and discovery. Read more »
ALS Computing Group Brings Machine Learning Models to Beamtimes around the World
The ALS computing team is developing tools to help users make the most of their beamtime and eliminate bottlenecks that currently exist. They have been traveling around the world and collaborating across facilities to develop advanced data processing solutions that will yield more meaningful data. Read more »
“Computer Vision” Review of X-Ray Movies Leads to New Insights
Using a type of machine learning called “computer vision” to mine data from x-ray movies, researchers made new discoveries about the reactivity of a material in rechargeable batteries. The results suggest that optimizing the carbon layer thickness on the electrode surface could help researchers to design more efficient batteries. Read more »