The DOI matches its Crossref record; 151 of the 151 references it cites were found in a registry or an index.
Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
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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“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.”
From Unsupervised machine learning in atomistic simulations, between predictions and understanding (2019) · cites this paper as [35]
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1 retracted
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Cited by
Sorted by citations
Computer Simulation of Liquids
Allen, M. P., & Tildesley, D. J. (2017) · doi:10.1093/oso/9780198803195.001.0001 · Cited by 20,945
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
Smith, J. S., Isayev, O., & Roitberg, A. E. (2017) · Chemical Science · doi:10.1039/c6sc05720a · Cited by 1,921
Perspective: Machine learning potentials for atomistic simulations
Behler, J. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4966192 · Cited by 1,489
Self-citationQuantum-chemical insights from deep tensor neural networks
Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R., & Tkatchenko, A. (2017) · Nature Communications · doi:10.1038/ncomms13890 · Cited by 1,445
Machine learning of accurate energy-conserving molecular force fields
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Metadata
- 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
- 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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