The DOI matches its Crossref record; 147 of the 147 references it cites were found in a registry or an index.
Machine learning for interatomic potential models
Open accessAbstract
The use of supervised machine learning to develop fast and accurate interatomic potential models is transforming molecular and materials research by greatly accelerating atomic-scale simulations with little loss of accuracy. Three years ago, Jörg Behler published a perspective in this journal providing an overview of some of the leading methods in this field. In this perspective, we provide an updated discussion of recent developments, emerging trends, and promising areas for future research in this field. We include in this discussion an overview of three emerging approaches to developing machine-learned interatomic potential models that have not been extensively discussed in existing reviews: moment tensor potentials, message-passing networks, and symbolic regression.
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Cites
Self-Consistent Equations Including Exchange and Correlation Effects
Kohn, W., & Sham, L. J. (1965) · Physical Review · doi:10.1103/physrev.140.a1133 · Cited by 64,135
Hohenberg, P., & Kohn, W. (1964) · Physical Review · doi:10.1103/physrev.136.b864 · Cited by 53,541
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Daw, M. S., & Baskes, M. I. (1984) · Physical Review B · doi:10.1103/physrevb.29.6443 · Cited by 7,263
ReaxFF: A Reactive Force Field for Hydrocarbons
van Duin, A. C. T., Dasgupta, S., Lorant, F., & Goddard, W. A. (2001) · The Journal of Physical Chemistry A · doi:10.1021/jp004368u · Cited by 6,402
Proceedings of the 25th international conference on Machine learning
Cohen, W. W., McCallum, A., & Roweis, S. T. (2008) · Cited by 5,691
Computer simulation of local order in condensed phases of silicon
Stillinger, F. H., & Weber, T. A. (1985) · Physical Review B · doi:10.1103/physrevb.31.5262 · Cited by 5,302
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
Behler, J., & Parrinello, M. (2007) · Physical Review Letters · doi:10.1103/physrevlett.98.146401 · Cited by 5,010
New empirical approach for the structure and energy of covalent systems
Tersoff, J. (1988) · Physical Review B · doi:10.1103/physrevb.37.6991 · Cited by 3,529
Neural Message Passing for Quantum Chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01212 · Cited by 3,440
Cited by
Hart, G. L. W., Mueller, T., Toher, C., & Curtarolo, S. (2021) · Nature Reviews Materials · doi:10.1038/s41578-021-00340-w · Cited by 703
Self-citationFan, Z., Zeng, Z., Zhang, C., Wang, Y., Song, K., Dong, H., Chen, Y., & Ala-Nissila, T. (2021) · Physical Review B · doi:10.1103/physrevb.104.104309 · Cited by 528
Quantum Chemistry in the Age of Machine Learning
Dral, P. O. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.9b03664 · Cited by 417
Automating alloy design and discovery with physics-aware multimodal multiagent AI
Ghafarollahi, A., & Buehler, M. J. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2414074122 · Cited by 72
Accurate large-scale simulations of siliceous zeolites by neural network potentials
Erlebach, A., Nachtigall, P., & Grajciar, L. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00865-w · Cited by 58
Metadata
- Authors
- Mueller, T., Hernandez, A., & Wang, C.
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 2020-02-05
- Volume
- 152
- Issue
- 5
- Pages
- 050902-050902
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W3004981459
- Cited by
- 363
- References
- 147
- FWCI
- 20.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
