Machine learning meets chemical physics

Author(s)
Michele Ceriotti, Cecilia Clementi, O. Anatole von Lilienfeld
Abstract

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. Published under license by AIP Publishing.

Organisation(s)
Computational Materials Physics
External organisation(s)
Freie Universität Berlin (FU), École polytechnique fédérale de Lausanne
Journal
Journal of Chemical Physics
Volume
154
No. of pages
5
ISSN
0021-9606
DOI
https://doi.org/10.1063/5.0051418
Publication date
04-2021
Peer reviewed
Yes
Austrian Fields of Science 2012
103018 Materials physics, 103006 Chemical physics
Portal url
https://ucris.univie.ac.at/portal/en/publications/machine-learning-meets-chemical-physics(a96cbaf2-8944-419a-84d6-c530c707b710).html