Events

Self-driving laboratories can compress years of materials discovery into weeks. Instead of relying on trial-and-error, they use algorithms to learn from each experiment and decide what to try next. When experiments are expensive and budgets are limited, choosing the next experiment well can have a large impact on the quality of the material ultimately discovered. This event will present results from a recent benchmark of 46 data-driven optimization solvers on 502 problems, ranging from one to 300 dimensions and spanning a wide variety of mathematical behavior.

Closed-loop discovery of functional molecules represents a new paradigm to new materials discovery. However, so far, AI-driven closed-loop discovery approaches have rarely yielded new chemical insight. I am leading a team to address this challenge under the Molecule Maker Lab Institute, one of the first AI Institutes in US. We connect autonomous synthesis, autonomous device printing and testing, and machine learning in a closed loop to drive molecular design of light harvesting small molecules towards high photostability.