Quantum Chemistry in the Age of Machine Learning

被引:275
|
作者
Dral, Pavlo O. [1 ,2 ]
机构
[1] Xiamen Univ, State Key Lab Phys Chem Solid Surfaces, Fujian Prov Key Lab Theoret & Computat Chem, Dept Chem, Xiamen 361005, Peoples R China
[2] Xiamen Univ, Coll Chem & Chem Engn, Xiamen 361005, Peoples R China
来源
JOURNAL OF PHYSICAL CHEMISTRY LETTERS | 2020年 / 11卷 / 06期
关键词
POTENTIAL-ENERGY SURFACES; MOLECULAR-DYNAMICS SIMULATIONS; COMBINED 1ST-PRINCIPLES CALCULATION; NEURAL-NETWORK POTENTIALS; MECHANICS/MOLECULAR MECHANICS; SCHRODINGER-EQUATION; ELECTRON-DENSITY; FORCE-FIELD; BIG DATA; MODELS;
D O I
10.1021/acs.jpclett.9b03664
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
As the quantum chemistry (QC) community embraces machine learning (ML), the number of new methods and applications based on the combination of QC and ML is surging. In this Perspective, a view of the current state of affairs in this new and exciting research field is offered, challenges of using machine learning in quantum chemistry applications are described, and potential future developments are outlined. Specifically, examples of how machine learning is used to improve the accuracy and accelerate quantum chemical research are shown. Generalization and classification of existing techniques are provided to ease the navigation in the sea of literature and to guide researchers entering the field. The emphasis of this Perspective is on supervised machine learning.
引用
收藏
页码:2336 / 2347
页数:12
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