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Machine learning meets chemical physics

Ceriotti, Michele ; Clementi, Cecilia ; Anatole von Lilienfeld, O.

The Journal of chemical physics, 2021-04, Vol.154 (16), p.160401-160401 [Periódico revisado por pares]

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Título:
Machine learning meets chemical physics
Autor: Ceriotti, Michele ; Clementi, Cecilia ; Anatole von Lilienfeld, O.
Assuntos: Machine learning ; Physical chemistry ; Physics
É parte de: The Journal of chemical physics, 2021-04, Vol.154 (16), p.160401-160401
Notas: SourceType-Other Sources-1
content type line 63
ObjectType-Editorial-2
ObjectType-Commentary-1
Descrição: 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.
Editor: United States: American Institute of Physics
Idioma: Inglês

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