The Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) is at the forefront of global change in science outcomes driven by artificial intelligence, automation and powerful data systems. By integrating these tools, researchers are transforming the speed and scale of discovery across fields, ranging from energy to material science to particle physics.
This integrated approach is not just about Berkeley Lab-IT promoting research to strengthen the country's scientific enterprises. By pioneering AI-enabled discovery platforms and sharing them across the research community, Berkeley Lab offers the tools and insights needed to solve some of the world's most pressing challenges, helping the US compete in the global race for innovation.
From accelerating material discovery to optimizing beamlines and more, Berkeley Lab uses AI to provide four ways to research faster, smarter, and more impactful:
Automating Discovery: Robotics for AI and Material Innovation
At the heart of the material is the formulation, synthesis and testing of thousands of potential compounds, a science-intensive process. AI is helping to speed up Berkeley Labs.
a-lab
At Berkeley Lab's automated materials facility, A-LAB, AI algorithms propose new compounds, and robots prepare and test them. This tight loop between machine intelligence and automation significantly reduces the time it takes to validate materials for use in technologies such as batteries and electronics.
Autobot
Exploration tools such as Autobot, a robotic system from Molecular Foundry, are being used to investigate new material for applications ranging from energy to quantum computing, making lab work faster and more flexible.
Smarter Equipment: AI for Real-Time Optimization
It operates accelerators and light sources that look like advanced research facilities. AI is on-the-spot support for regulating equipment at Berkeley Labs, making it more stable, efficient and productive.
Berkeley Lab Laser Accelerator (Bella)
Machine learning models are used in Bella to optimize and stabilize lasers and electron beams, improving performance, reducing manual calibration times and providing new opportunities for scientific and industrial applications.
Advanced Light Source Upgrade (ALS-U)
In ALS, deep learning AI-based controls are applied to synchrotron operations to optimize beam performance. These techniques will be implemented in future ALS-U. ALS-U serves American industries and researchers, providing one of the brightest soft X-ray sources in the world.
Accelerate data: Automatic analysis
Modern science generates huge amounts of data. Berkeley Lab, AI, Automation, and Machine Learning analyze this data faster with scientists in real time, tailoring experiments on the fly, and generating useful findings faster than before.
Supercomputing for on-demand insights
Data from light sources, microscopes, and telescopic models flows into Berkeley Lab's supercomputing facility and can be processed almost instantly. At Molecular Foundry's National Center for Electron Microscy, distillers are collected directly from the microscope, a new web-based platform called Distiller Streams Data, which are analyzed within minutes directly into the National Energy Research Scientific Computing Center (NERSC) Perlmutter Supercomputer, which researchers refine their experiments while they are still in progress. This new feature allows real-time decision-making during experiments, saving time and money, and has the potential for a wide range of applications in many scientific fields.
Fusion research
In NERSC, machine learning is used to predict the behavior of particles in fusion plasmas and may inform future fusion reactor live control systems.
AI for network optimization in ESNET
Berkeley Lab's high-performance network, Esnet uses AI to predict and troubleshoot network traffic, ensuring seamless, fast collaboration across labs and research partners across the nation. As the use of huge AI-driven datasets increases, ESNET is improving system innovation to support the future of data-intensive collaboration at scale that has never been seen before.
Generating breakthroughs: AI as a co-creator
Research into AI predictions
AI is more than just an accelerator. It's a collaborator. Scientists at Berkeley Lab also help test and criticize AI-driven discoveries from beyond the lab. Recently, medical researchers have used AI to design new enzymes. To investigate the accuracy of its design, they turned to advanced light sources. There, scientists looked at samples to bridge the gap between AI prediction and reality. This new approach could accelerate the development of novel proteins for almost everything, including medical, energy and other industrial applications.
AI embedded in the future of science
These innovations are part of the growth in our thinking about how science is done. From experimental design to analysis and operations, AI helps Berkeley Lab teams do more, allowing researchers to focus on discovery, processing repetitive tasks, real-time analysis of large data sets, and more. By integrating AI with robotics, instrumentation and data systems, Berkeley Lab is building a smarter, faster science infrastructure ready to solve tomorrow's biggest challenges.
ALS, NERSC, ESNET, and Molecular Foundry are DOE offices for science users facilities.
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