Retrospective on a decade of machine learning for chemical discovery (2020-09-29T00:00:00.000000Z)

TL;DR

This paper considers selected studies of electronic structure, interatomic potentials, and chemical compound space in chronological order to highlight specific achievements of machine learning models in the field of computational chemistry.

Abstract

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.

Authors

K. Burke

4 papers

O. von Lilienfeld

1 papers

TL;DR

Abstract

Authors

References32 items

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