Ceriotti Michele, Clementi Cecilia, Anatole von Lilienfeld O
Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
Department of Physics, Freie Universität Berlin, Arnimallee 14, 14195 Berlin, Germany.
J Chem Phys. 2021 Apr 28;154(16):160401. doi: 10.1063/5.0051418.
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.
近年来，将统计学习技术应用于化学问题的做法已获得了强劲的发展势头。这在物理化学领域尤为明显，在该领域中，经验主义与基于物理的理论之间的平衡传统上一直更倾向于后者。在这篇关于“机器学习与化学物理相遇”专题特刊的客座编辑文章中，我们首先给出了简要的基本原理，然后概述了所涵盖的主题。最后我们将给出一些一般性评论。
