Machine Learning for Molecular Simulation

Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for an ML revolution and have already been profoundly affected by the application of existing ML methods. Here we review recent ML methods for molecular simulation, with particular focus on (deep) neural networks for the prediction of quantum-mechanical energies and forces, on coarse-grained molecular dynamics, on the extraction of free energy surfaces and kinetics, and on generative network approaches to sample molecular equilibrium structures and compute thermodynamics. To explain these methods and illustrate open methodological problems, we review some…

• NS
  National Science Foundation

  Awards: CHE-1900374, CHE-1265929, 390685689, 1740990, CHE-1740990, 1900374
• EC
  European Commission

  Awards: CoG 772230, ERC CoG 772230, 772230
• DF
  Deutsche Forschungsgemeinschaft

  Awards: CRC1114/A04, 01GQ0850, EXC 2046, GRK2433, 01GQ1115, EF1-2, CRC1114, 01IS14013A-E, GRK2433 DAEDALUS, 390685689, EXC 2046/1
• ES
  Einstein Stiftung Berlin
• DO
  Division of Chemistry

  Awards: CHE-1265929, CHE-1900374, 1265929, CHE-1740990