Retrospective on a decade of machine learning for chemical discovery

Over the last decade, we have witnessed the emergence of ever more machine learning applications in all aspects of the chemical sciences. Here, we highlight specific achievements of machine learning models in the field of computational chemistry by considering selected studies of electronic structur...

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Detalles Bibliográficos
Autores principales: von Lilienfeld, O. Anatole, Burke, Kieron
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Comment
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7525448/
https://www.ncbi.nlm.nih.gov/pubmed/32994393
http://dx.doi.org/10.1038/s41467-020-18556-9
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author von Lilienfeld, O. Anatole
Burke, Kieron
author_facet von Lilienfeld, O. Anatole
Burke, Kieron
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description Over the last decade, we have witnessed the emergence of ever more machine learning applications in all aspects of the chemical sciences. Here, we highlight specific achievements of machine learning models in the field of computational chemistry by considering selected studies of electronic structure, interatomic potentials, and chemical compound space in chronological order.
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spelling pubmed-75254482020-10-19 Retrospective on a decade of machine learning for chemical discovery von Lilienfeld, O. Anatole Burke, Kieron Nat Commun Comment Over the last decade, we have witnessed the emergence of ever more machine learning applications in all aspects of the chemical sciences. Here, we highlight specific achievements of machine learning models in the field of computational chemistry by considering selected studies of electronic structure, interatomic potentials, and chemical compound space in chronological order. Nature Publishing Group UK 2020-09-29 /pmc/articles/PMC7525448/ /pubmed/32994393 http://dx.doi.org/10.1038/s41467-020-18556-9 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Comment
von Lilienfeld, O. Anatole
Burke, Kieron
Retrospective on a decade of machine learning for chemical discovery
title Retrospective on a decade of machine learning for chemical discovery
title_full Retrospective on a decade of machine learning for chemical discovery
title_fullStr Retrospective on a decade of machine learning for chemical discovery
title_full_unstemmed Retrospective on a decade of machine learning for chemical discovery
title_short Retrospective on a decade of machine learning for chemical discovery
title_sort retrospective on a decade of machine learning for chemical discovery
topic Comment
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7525448/
https://www.ncbi.nlm.nih.gov/pubmed/32994393
http://dx.doi.org/10.1038/s41467-020-18556-9
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