Talk: Designing New Energy Materials with Machine Learning
1:30 - 2pm
Designing new materials is vital to address pressing societal challenges in health, en-ergy and sustainability. However, the space of hypothetical materials to be considered is incredibly large, and only a small fraction of possible compounds can ever be tested experimentally. Computational techniques, in particular, atomistic simulation and machine learning (ML), offer an avenue to rapidly invent new materials and navigate this enormous space. Together, they can be used to infer complex design principles and identify high-quality candidates for experimental validation more rapidly and efficiently than trial-and-error experimentation. Here, we will discuss how ML tools combined with physics-based simulation has enabled materials innovation in areas like catalysis, batteries or therapeutics, and highlight how hyperparameter optimization can make or break a materials design pipeline.