Benjamin X. Shi

Distilling First-Principles Accuracy into Compact Machine Learning Potentials for Condensed-Phase Chemistry

Abstract Accurate machine learning interatomic potentials (MLIPs) have made first-principles-quality potential energy surfaces increasingly accessible for condensed-phase chemistry, but their inference cost can still limit the sampling needed to compute experimentally relevant ob...