Journal
NATURE COMMUNICATIONS
Volume 11, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41467-020-18556-9
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Funding
- Swiss National Science foundation [407540_167186 NFP 75 Big Data, 200021_175747]
- European Research Council (ERC-CoG grant QML)
- NSF [CHE 1856165]
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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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