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

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

Behler, Jörg·2011·Physical Chemistry Chemical Physics

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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Crossref · 10.1039/c1cp21668f

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  • General-purpose and systematically-convergent descriptions of chemical environments provide a starting point that is considerably less biased than descriptors based on complicated heuristics that are designed from the outset to identify e.g. a stacking order, or the coordination of a site.

    “General-purpose and systematically-convergent descriptions of chemical environments, such as those that are typically used to fit potentials or predict molecular properties 31,35 , provide a starting point that is considerably less biased than descriptors based on complicated heuristics that are designed from the outset to identify e.g. a stacking order, or the coordination of a site.”

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Authors
Behler, J.
Affiliations
Ruhr University Bochum (DE)
Corresponding author
Behler, Jörg
Venue
Physical Chemistry Chemical Physics
Published
2011
Volume
13
Issue
40
Pages
17930
ISSN
  • 1463-9076 (ISSN-L)
  • 1463-9084
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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