Cited by 781Machine Learning in Materials ScienceAdvanced Chemical Physics StudiesAdvanced Physical and Chemical Molecular Interactions

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Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations

Behler, Jörg·2011·Physical Chemistry Chemical Physics

DOI 10.1039/c1cp21668f

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Abstract

The accuracy of the results obtained in molecular dynamics or Monte Carlo simulations crucially depends on a reliable description of the atomic interactions. A large variety of efficient potentials has been proposed in the literature, but often the optimum functional form is difficult to find and strongly depends on the particular system. In recent years, artificial neural networks (NN) have become a promising new method to construct potentials for a wide range of systems. They offer a number of advantages: they are very general and applicable to systems as different as small molecules, semiconductors and metals; they are numerically very accurate and fast to evaluate; and they can be constructed using any electronic structure method. Significant progress has been made in recent years and a number of successful applications demonstrate the capabilities of neural network potentials. In this Perspective, the current status of NN potentials is reviewed, and their advantages and limitations are discussed.

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Influential
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Background
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Self-citation
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“Therefore, machine learning (ML) has provided an alternative avenue [12–15].”
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Authors: Behler, J.
Affiliations: Ruhr University Bochum (DE)
Corresponding author: Behler, Jörg
ORCID: Behler, Jörg
Venue: Physical Chemistry Chemical Physics
Publisher: Royal Society of Chemistry (RSC)
Published: 2011
Volume: 13
Issue: 40
Pages: 17930
ISSN: 1463-9076 (ISSN-L)
1463-9084
DOI: 10.1039/c1cp21668f
OpenAlex: W2058370262
Cited by: 781
References: 151
Citations by year: 2026: 27 · 2025: 58 · 2024: 54 · 2023: 71 · 2022: 68 · 2021: 79 · 2020: 89 · 2019: 66 · 2018: 58 · 2017: 88 · 2016: 33 · 2015: 28 · 2014: 27 · 2013: 23 · 2012: 11
FWCI: 0.93
Type: journal article
Language: en
Open access: closed
Locations: Physical Chemistry Chemical Physics (publishedVersion)
PubMed (publishedVersion)
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 › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
Keywords: neural network potentials, interatomic potentials, potential energy surface, molecular dynamics, Monte Carlo simulation, interatomic interactions, electronic structure methods, large-scale simulation, machine learning for materials
MeSH terms: Computer Simulation, Metals, Models, Molecular, Neural Networks, Computer, Small Molecule Libraries

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Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations

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