Machine learning meets chemical physics

被引:36
作者
Ceriotti, Michele [1 ]
Clementi, Cecilia [2 ]
Anatole von Lilienfeld, O. [3 ]
机构
[1] Ecole Polytech Fed Lausanne, Lab Computat Sci & Modeling, IMX, CH-1015 Lausanne, Switzerland
[2] Free Univ Berlin, Dept Phys, Arnimallee 14, D-14195 Berlin, Germany
[3] Univ Vienna, Fac Phys, Kolingasse 14-18, A-1090 Vienna, Austria
关键词
D O I
10.1063/5.0051418
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
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.
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页数:5
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