Accelerating explicit solvent models of heterogeneous catalysts with machine learning interatomic potentials

Abstract

Active learning of machine learning interatomic potentials enables dynamic modelling of adsorption and reactions at explicitly solvated heterogeneous catalyst interfaces with near ab initio accuracy and greatly reduced computational cost.