The DOI matches its Crossref record; 290 of the 290 references it cites were found in a registry or an index.
Physics-Inspired Structural Representations for Molecules and Materials
Open accessAbstract
. The development of atomic-scale representations has played, and continues to play, a central role in the success of machine-learning methods for chemistry and materials science. This review summarizes the current understanding of the nature and characteristics of the most commonly used structural and chemical descriptions of atomistic structures, highlighting the deep underlying connections between different frameworks and the ideas that lead to computationally efficient and universally applicable models. It emphasizes the link between properties, structures, their physical chemistry, and their mathematical description, provides examples of recent applications to a diverse set of chemical and materials science problems, and outlines the open questions and the most promising research directions in the field.
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Computational chemistry for water-splitting electrocatalysis
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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
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Metadata
- Authors
- Musil, F., Grisafi, A., Bartók, A. P., Ortner, C., Csányi, G., & Ceriotti, M.
- Venue
- Chemical Reviews
- Publisher
- American Chemical Society (ACS)
- Published
- 2021-07-26
- Volume
- 121
- Issue
- 16
- Pages
- 9759-9815
- ISSN
- 0009-2665 (ISSN-L)
- 1520-6890
- OpenAlex
- W3120951562
- Cited by
- 523
- References
- 290
- FWCI
- 4.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Musil, F., Grisafi, A., Bartók, A. P., Ortner, C., Csányi, G., & Ceriotti, M. (2021). Physics-Inspired Structural Representations for Molecules and Materials. Chemical Reviews. https://doi.org/10.1021/acs.chemrev.1c00021
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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