Controlling and designing molecular materials with generative machine learning

Building and controlling new chemical matter is the foundational challenge in chemistry and the confluence of two tools, nonequilibrium control and machine learning, is providing exciting opportunities to expand our capabilities for control. In this talk, I will describe two efforts that sit at the interface between chemistry and machine learning to shed light on how we can leverage chemical insight to inform models for molecular design. First, I will discuss adapting “foundation models” with experimental data to inform the design of both proteins and small molecules. In the second part, I will describe how external, nonequilibrium controls can push biomaterial design beyond its static limits. 

Speaker: Grant Rotskoff, Stanford University