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Machine Learning and Artificial Intelligence

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Developing novel, robust, and interpretable AI and learning methods; applying and adapting advances in AI to the complexity of science; enabling the deployment of AI applications at large computing scales.

Berkeley Lab's research into machine learning builds on its foundational work in mathematics to develop methods that are consistent with physical laws, robust in the presence of noisy or biased data, and capable of being interpreted and explained in scientifically meaningful ways.

New Methods

Active Learning

Physics Constrained Machine Learning 

Optimization

Science Applications

Surrogate Models

Supercomputing-Scale AI

Secure Machine Learning & ML for Security

As a Department of Energy National Laboratory, we develop and share the algorithms, software, tools, and libraries that are foundational to scientific machine learning. We gather, organize and store huge scientific datasets in areas such as materials, energy, environment, biology, genomics, and astronomy. Coupled with high-performance computing optimized for machine learning and the advanced networking capabilities of Berkeley Lab’s national user facilities – NERSC, and ESnet – our researchers are cracking tough science problems with artificial intelligence techniques.