The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Gaussian Process Regression for Materials and Molecules
Open accessAbstract
We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian Approximation Potential (GAP) framework; beyond this, we also discuss the fitting of arbitrary scalar, vectorial, and tensorial quantities. Methodological aspects of reference data generation, representation, and regression, as well as the question of how a data-driven model may be validated, are reviewed and critically discussed. A survey of applications to a variety of research questions in chemistry and materials science illustrates the rapid growth in the field. A vision is outlined for the development of the methodology in the years to come.
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Cites
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Cited by
Small data machine learning in materials science
Xu, P., Ji, X., Li, M., & Lu, W. (2023) · npj Computational Materials · doi:10.1038/s41524-023-01000-z · Cited by 866
A foundation model for atomistic materials chemistry
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Self-citationA foundation model for atomistic materials chemistry
Batatia, I., Benner, P., Chiang, Y., Elena, A. M., Kovács, D. P., Riebesell, J., Advincula, X. R., Asta, M., Avaylon, M., Baldwin, W. J., et al. (2025) · The Journal of Chemical Physics · doi:10.1063/5.0297006 · Cited by 566
Self-citationCHARMM at 45: Enhancements in Accessibility, Functionality, and Speed
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Deep learning metal complex properties with natural quantum graphs
Kneiding, H., Lukin, R., Lang, L., Reine, S., Pedersen, T. B., De Bin, R., & Balcells, D. (2023) · Digital Discovery · doi:10.1039/d2dd00129b · Cited by 61
Metadata
- Authors
- Deringer, V. L., Bartók, A. P., Bernstein, N., Wilkins, D. M., Ceriotti, M., & Csányi, G.
- Venue
- Chemical Reviews
- Publisher
- American Chemical Society (ACS)
- Published
- 2021-08-16
- Volume
- 121
- Issue
- 16
- Pages
- 10073-10141
- ISSN
- 0009-2665 (ISSN-L)
- 1520-6890
- OpenAlex
- W3189164715
- Cited by
- 1,221
- References
- 37
- FWCI
- 8.25
- Type
- journal article
- Language
- en
- Open access
- hybrid
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