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Elucidating an Atmospheric Brown Carbon Species—Toward Supplanting Chemical Intuition with Exhaustive Enumeration and Machine Learning

https://doi.org/10.1021/acs.est.1c00885

Tapavicza, Enrico ; von Rudorff, Guido Falk ; De Haan, David O. ; Contin, Mario ; George, Christian ; Riva, Matthieu ; von Lilienfeld, O. Anatole ( June 2021 , Environmental Science & Technology)
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Retrospective on a decade of machine learning for chemical discovery

https://doi.org/tps://doi.org/10.1038/s41467-020-18556-9 | www.nature.com/naturecommunications

von Lilienfeld, O. Anatole ; and Burke, Kieron ( January 2020 , Nature communications)

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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Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry

https://doi.org/10.1063/1.5043213

Rupp, Matthias ; von Lilienfeld, O. Anatole ; Burke, Kieron ( June 2018 , The Journal of Chemical Physics)
Constant size descriptors for accurate machine learning models of molecular properties

https://doi.org/10.1063/1.5020441

Collins, Christopher R. ; Gordon, Geoffrey J. ; von Lilienfeld, O. Anatole ; Yaron, David J. ( June 2018 , The Journal of Chemical Physics)
Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning

https://doi.org/10.1063/1.5009502

Bereau, Tristan ; DiStasio, Jr., Robert A. ; Tkatchenko, Alexandre ; von Lilienfeld, O. Anatole ( June 2018 , The Journal of Chemical Physics)