Machine learning with and for molecular dynamics simulations

Abstract

From simple clustering techniques to more sophisticated neural networks, the use of machine learning has become a valuable tool in many fields of chemistry in the past decades. Here, we describe two different ways in which we explore the combination of machine learning (ML) and molecular dynamics (MD) simulations. One topic focuses on how the information in MD simulations can be encoded such that it can be used as input to train ML models for the quantitative understanding of molecular systems. The second topic addresses the utilization of machine learning to improve the set-up, interpretation, as well as accuracy of MD simulations.