Fundamental breakthroughs across many scientific disciplines are becoming increasingly rare (1). At the same time, challenges related to the reproducibility and scalability of experiments, especially in the natural sciences (2,3), remain significant obstacles. For years, automating scientific experiments has been viewed as the key to solving this problem. However, existing solutions are often rigid and complex, designed to address specific experimental tasks with little adaptability to protocol changes. With advancements in robotics and artificial intelligence, new possibilities are emerging to tackle this challenge in a more flexible and human-centric manner.

"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." 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. 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.