Machine learning meets chemical physics

Ceriotti, Michele and Clementi, Cecilia and von Lilienfeld, O. Anatole. (2021) Machine learning meets chemical physics. Journal of Chemical Physics, 154 (16). p. 160401.

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Official URL: https://edoc.unibas.ch/87401/

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Over recent years, the use of statistical learning techniques applied to chemical problems has gained substantial momentum. This is particularly apparent in the realm of physical chemistry, where the balance between empiricism and physics-based theory has traditionally been rather in favor of the latter. In this guest Editorial for the special topic issue on "Machine Learning Meets Chemical Physics," a brief rationale is provided, followed by an overview of the topics covered. We conclude by making some general remarks.
Faculties and Departments:05 Faculty of Science > Departement Chemie > Former Organization Units Chemistry > Physikalische Chemie (Lilienfeld)
UniBasel Contributors:von Lilienfeld, Anatole
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:AIP Publishing
Note:Publication type according to Uni Basel Research Database: Journal article
Identification Number:
Last Modified:27 Jan 2022 16:33
Deposited On:27 Jan 2022 16:33

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