Modeling and simulation
Machine learning / AI
Software enabling science
“Berkeley Lab is unique because its machine learning expertise is reasonably well established, and its tradition of team science means that we can work with researchers to apply these methods to scientific problems.”
“Although much of the time and effort spent in the software maintenance is not reflected in our research publication list, it is more than rewarding to see the wide use of this software in both the high-end scientific world and the commercial world.”
“I think one of the things Berkeley Lab does well is allow people to make collaborations that advance science much more efficiently.”
New calculations from Google DeepMind grow Berkeley Lab’s Materials Project, an open-access resource that scientists use to invent new materials for future technologies. Some of the computations were used alongside data from the Materials Project to test A-Lab, a facility at Berkeley Lab where artificial intelligence guides robots in making new materials. A-Lab’s first results show that the autonomous lab can quickly discover novel materials with minimal human input.
First developed about 80 years ago, machine learning is a type of AI centered on programs — called algorithms — that can teach themselves different ways of processing data after they are trained on sample datasets.
Berkeley Lab Research Scientist Mariam Kiran uses deep reinforcement learning and innovative multi-objective optimization techniques to train network controllers to predict network traffic and improve traffic engineering.