Open AccessMachine Learning with and for Molecular Dynamics Simulations
Author(s) -
Sereina Riniker,
Shuzhe Wang,
Patrick Bleiziffer,
Lennard Böselt,
Carmen Esposito
Publication year - 2019
Publication title -
chimia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.387
H-Index - 55
eISSN - 2673-2424
pISSN - 0009-4293
DOI - 10.2533/chimia.2019.1024
Subject(s) - computer science , artificial intelligence , machine learning , set (abstract data type) , molecular dynamics , cluster analysis , interpretation (philosophy) , simple (philosophy) , field (mathematics) , artificial neural network , chemistry , computational chemistry , mathematics , philosophy , epistemology , pure mathematics , programming language
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.