The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
On representing chemical environments
Open accessAbstract
We review some recently published methods to represent atomic neighborhood environments, and analyze their relative merits in terms of their faithfulness and suitability for fitting potential energy surfaces. The crucial properties that such representations (sometimes called descriptors) must have are differentiability with respect to moving the atoms and invariance to the basic symmetries of physics: rotation, reflection, translation, and permutation of atoms of the same species. We demonstrate that certain widely used descriptors that initially look quite different are specific cases of a general approach, in which a finite set of basis functions with increasing angular wave numbers are used to expand the atomic neighborhood density function. Using the example system of small clusters, we quantitatively show that this expansion needs to be carried to higher and higher wave numbers as the number of neighbors increases in order to obtain a faithful representation, and that variants of the descriptors converge at very different rates. We also propose an altogether different approach, called Smooth Overlap of Atomic Positions, that sidesteps these difficulties by directly defining the similarity between any two neighborhood environments, and show that it is still closely connected to the invariant descriptors. We test the performance of the various representations by fitting models to the potential energy surface of small silicon clusters and the bulk crystal.
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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 [31]
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Cites
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
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Self-citationQuantum Theory Of Angular Momemtum
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Recent advances and applications of machine learning in solid-state materials science
Schmidt, J., Marques, M. R. G., Botti, S., & Marques, M. A. L. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0221-0 · Cited by 2,786
Machine learning and the physical sciences
Carleo, G., Cirac, J. I., Cranmer, K. S., Daudet, L., Schuld, M., Tishby, N., Vogt-Maranto, L., & Zdeborová, L. (2019) · Reviews of Modern Physics · doi:10.1103/revmodphys.91.045002 · Cited by 2,640
SchNet – A deep learning architecture for molecules and materials
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., & Müller, K.-R. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5019779 · Cited by 2,217
Wang, H., Zhang, L., Han, J., & Weinan, E. (2018) · Computer Physics Communications · doi:10.1016/j.cpc.2018.03.016 · Cited by 2,213
Inverse molecular design using machine learning: Generative models for matter engineering
Sánchez-Lengeling, B., & Aspuru‐Guzik, A. (2018) · Science · doi:10.1126/science.aat2663 · Cited by 2,177
Metadata
- Authors
- Bartók, A. P., Kondor, R., & Csányi, G.
- Affiliations
- University of Cambridge (GB)
- University of Chicago (US)
- Venue
- Physical Review B
- Publisher
- American Physical Society (APS)
- Published
- 2013-05-28
- Volume
- 87
- Issue
- 18
- ISSN
- 1098-0121 (ISSN-L)
- 1550-235X
- OpenAlex
- W2029413789
- Cited by
- 2,572
- References
- 60
- Citations by year
- 2026: 292 · 2025: 350 · 2024: 367 · 2023: 365 · 2022: 303 · 2021: 270 · 2020: 257 · 2019: 163 · 2018: 105 · 2017: 48 · 2016: 19 · 2015: 11 · 2014: 13 · 2013: 4
- FWCI
- 7.07
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Physical Review B (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- arXiv (Cornell University)
- Funders
- Engineering and Physical Sciences Research Council (EP/K014560/1)
- 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
- potential energy surface fitting, basis function expansion, silicon clusters, small clusters
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