Machine learning for molecular simulation

Frank Noé, Alexandre Tkatchenko, Klaus Robert Müller, Cecilia Clementi

Research output: Contribution to journalReview articlepeer-review

459 Citations (Scopus)


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 important principles of molecular physics and describe how they can be incorporated into ML structures. Finally, we identify and describe a list of open challenges for the interface between ML and molecular simulation.

Original languageEnglish
Pages (from-to)361-390
Number of pages30
JournalAnnual Review of Physical Chemistry
Publication statusPublished - 2020 Apr 20

Bibliographical note

Funding Information:
We gratefully acknowledge funding from the European Commission (ERC CoG 772230 "ScaleCell" to F.N. and ERC CoG grant BeStMo to A.T.), Deutsche Forschungsgemeinschaft (CRC1114/A04 to F.N.; EXC 2046/1, project ID 390685689, to K.-R.M.; and GRK2433 DAEDALUS to F.N. and K.-R.M.), the MATH+ Berlin Mathematics research center (AA1-6 to F.N. and EF1-2 to F.N. and K.-R.M.), Einstein Foundation Berlin (Einstein Visiting Fellowship to C.C.), the National Science Foundation (grants CHE-1265929, CHE-1740990, CHE-1900374, and PHY-1427654 to C.C.), theWelch Foundation (grant C-1570 to C.C.), an Institute for Information and Communications Technology Planning and Evaluation grant funded by the Korean government (2017-0-00451 and 2017-0-01779 to K.-R.M.), and the German Ministry for Education and Research (grants 01IS14013A-E, 01GQ1115, and 01GQ0850 to K.-R.M.).We thank Stefan Chmiela and Kristof Schtt for help with Figures 1 and 5.

Publisher Copyright:
Copyright © 2020 by Annual Reviews. All rights reserved.


  • coarse graining
  • kinetics
  • machine learning
  • molecular simulation
  • neural networks
  • quantum mechanics

ASJC Scopus subject areas

  • General Medicine


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