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Physics-inspired structural representations for molecules and materials
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Musil, Felix, Grisafi, Andrea, Bartók, Albert P., Ortner, Christoph, Csányi, Gábor and Ceriotti, Michele (2021) Physics-inspired structural representations for molecules and materials. Chemical Reviews, 121 (16). pp. 9759-9815. doi:10.1021/acs.chemrev.1c00021 ISSN 0009-2665.
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WRAP-physics-inspired-structural-representations-molecules-materials-Bartók-2021.pdf - Accepted Version - Requires a PDF viewer. Download (11Mb) | Preview |
Official URL: http://dx.doi.org/10.1021/acs.chemrev.1c00021
Abstract
The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atoms into a suitable representation. 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.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QC Physics Q Science > QD Chemistry T Technology > TA Engineering (General). Civil engineering (General) |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Physics | ||||||||
| Library of Congress Subject Headings (LCSH): | Group theory, Chemical structure -- Data processing, Materials science -- Data processing, Machine learning , Molecules | ||||||||
| Journal or Publication Title: | Chemical Reviews | ||||||||
| Publisher: | American Chemical Society | ||||||||
| ISSN: | 0009-2665 | ||||||||
| Official Date: | 25 August 2021 | ||||||||
| Dates: |
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| Volume: | 121 | ||||||||
| Number: | 16 | ||||||||
| Page Range: | pp. 9759-9815 | ||||||||
| DOI: | 10.1021/acs.chemrev.1c00021 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | “This document is the Accepted Manuscript version of a Published Work that appeared in final form in Chemical Reviews, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see [insert ACS Articles on Request author-directed link to Published Work, see http://pubs.acs.org/page/policy/articlesonrequest/index.html].” | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Copyright Holders: | Copyright © 2021 The Authors. Published by American Chemical Society | ||||||||
| Date of first compliant deposit: | 20 July 2021 | ||||||||
| Date of first compliant Open Access: | 26 July 2022 | ||||||||
| RIOXX Funder/Project Grant: |
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