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Cited by 1,647Machine Learning in Materials ScienceAdvanced Chemical Physics StudiesComputational Drug Discovery Methods

Machine Learning Force Fields

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Unke, Oliver T., Chmiela, Stefan, Sauceda, Huziel E., Gastegger, Michael, Poltavsky, Igor, Schütt, Kristof T., Tkatchenko, Alexandre, Müller, Klaus-Robert·2021·Chemical Reviews
DOI 10.1021/acs.chemrev.0c01111PMC PMC8391964
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Abstract

methods and the efficiency of classical FFs. The key idea is to learn the statistical relation between chemical structure and potential energy without relying on a preconceived notion of fixed chemical bonds or knowledge about the relevant interactions. Such universal ML approximations are in principle only limited by the quality and quantity of the reference data used to train them. This review gives an overview of applications of ML-FFs and the chemical insights that can be obtained from them. The core concepts underlying ML-FFs are described in detail, and a step-by-step guide for constructing and testing them from scratch is given. The text concludes with a discussion of the challenges that remain to be overcome by the next generation of ML-FFs.

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  • Machine learning potentials (MLPs) interpolate the potential energy surface at the level of an ab initio training set.

    “An emerging alternative to analytical force fields is represented by machine learning potentials (MLPs), which interpolate the potential energy surface (PES) at the level of an ab initio training set22–24.”

    From A reactive neural network framework for water-loaded acidic zeolites (2024) · cites this paper as [22]

  • MLPs are expected to have only limited capability to reliably describe configurations and energetics in extrapolated or sparsely interpolated regions of the PES, which often coincide with high-energy transition state configurations.

    “However, MLPs are expected to have only limited capability to reliably describe configurations and energetics in extrapolated or sparsely interpolated regions of the potential energy surface22, which often coincide with the high-energy transition state configurations.”

    From A reactive neural network framework for water-loaded acidic zeolites (2024) · cites this paper as [22]

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Cited by

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Authors
Unke, O. T., Chmiela, S., Sauceda, H. E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko, A., & Müller, K.-R.
Affiliations
  • Technische Universität Berlin (DE)
  • University of Luxembourg (LU)
  • Google (United States) (US)
Corresponding author
  • Tkatchenko, Alexandre
  • Müller, Klaus-Robert
ORCID
Venue
Chemical Reviews
Publisher
American Chemical Society (ACS)
Published
2021-03-11
Volume
121
Issue
16
Pages
10142-10186
ISSN
  • 0009-2665 (ISSN-L)
  • 1520-6890
DOI
10.1021/acs.chemrev.0c01111
PMC
PMC8391964
OpenAlex
W3093149769
Cited by
1,647
References
22
Citations by year
2026: 388 · 2025: 403 · 2024: 319 · 2023: 270 · 2022: 177 · 2021: 87 · 2020: 2 · 2019: 1
FWCI
13.07
Type
journal article
Language
en
Open access
Licence
Locations
  • Chemical Reviews (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Open Repository and Bibliography (University of Luxembourg) (submittedVersion, cc-by-nc-sa)
  • DepositOnce (submittedVersion, cc-by-nc-nd)
  • arXiv (Cornell University)
  • Technische Universität Berlin – Universitätsbibliothek
Funders
  • Bundesministerium f?r Bildung und Forschung (01GQ0850, 01GQ1115, 01IS14013A-E, 01IS18025A, 01IS18037A, 031L0207D)
  • Schweizerischer Nationalfonds zur F?rderung der Wissenschaftlichen Forschung (P2BSP2_188147)
  • Deutsche Forschungsgemeinschaft (EXC 2046/1, Math+)
  • Institute for Information and Communications Technology Promotion (2017-0-00451)
  • H2020 European Research Council
  • Korea University (019-0-00079)
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
machine learning interatomic potentials, potential energy surface, chemical bonding, interatomic interactions, molecular dynamics simulation

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