Machine Learning for Molecular Simulation
Open accessAbstract
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.
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Machine learning has already been making a significant impact on the development of approximate methods for complex atomic systems.
“machine learning has already been making a significant impact on the development of approximate methods for complex atomic systems.”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [186]
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Sorted by citations
Emerging materials intelligence ecosystems propelled by machine learning
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Molecular dynamics simulation of an entire cell
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“Furthermore, machine-learning can be invoked to extract interaction patterns and other emergent behavior that might be missed by standard analysis tools (Noé et al., 2020; Wang et al., 2020; Kaptan and Vattulainen, 2022).”
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Quo vadis multiscale modeling in reaction engineering? – A perspective
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Protein representations: Encoding biological information for machine learning in biocatalysis
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- Authors
- Noé, F., Tkatchenko, A., Müller, K.-R., & Clementi, C.
- Affiliations
- Center for Theoretical Biological Physics (US)
- University of Luxembourg (LU)
- Korea University (KR)
- Venue
- Annual Review of Physical Chemistry
- Publisher
- Annual Reviews
- Published
- 2020-04-20
- Volume
- 71
- Issue
- 1
- Pages
- 361-390
- ISSN
- 0066-426X (ISSN-L)
- 1545-1593
- OpenAlex
- W2988055229
- Cited by
- 956
- References
- 136
- Citations by year
- 2026: 121 · 2025: 208 · 2024: 147 · 2023: 176 · 2022: 124 · 2021: 133 · 2020: 45 · 2019: 2
- FWCI
- 6.14
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Annual Review of Physical Chemistry (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- National Science Foundation (390685689, 1740990, 1265929, 1427654, 1900374)
- European Commission (CoG 772230, ERC CoG 772230, 772230)
- Deutsche Forschungsgemeinschaft (CRC1114/A04, EXC-2046, 01GQ0850, EXC‐2046/1, GRK2433, 01GQ1115, EF1-2, 01IS14013A-E, CRC 1114, GRK2433 DAEDALUS, 390685689)
- Einstein Stiftung Berlin
- Division of Chemistry (CHE-1265929, CHE-1900374, 1265929, CHE-1740990)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- deep neural network, force field parameterization, coarse-grained molecular dynamics, free energy landscape, reaction kinetics, generative neural networks, thermodynamics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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