From Espaloma to SAKE: To brew, distill, and mix force fields with balanced briskness, smoothness, and intricacy.
Yuanqing Wang
Force fields (FF)—the (parametrized) mapping from geometry to energy, are a crucial component of molecular dynamics (MD) simulations, whose associated Boltzmann-like target probability densities are sampled to estimate ensemble observables, to harvest quantitative insights of the system. State-of-the-art force fields are either fast (molecular mechanics, MM-based) or accurate (quantum mechanics, QM-based), but seldom both. Here, leveraging graph-based machine learning and incorporating inductive biases crucial to chemical modeling, we approach the balance between accuracy and speed from two angles---to make MM more accurate and to make machine learning force fields faster.
Materials and Chemistry
Grand Salon C