Quantum Chemistry in the Age of Machine Learning

被引:276
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
相关论文
共 251 条
[1]  
Abadi M, 2016, PROCEEDINGS OF OSDI'16: 12TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION, P265
[2]   PES-Learn: An Open-Source Software Package for the Automated Generation of Machine Learning Models of Molecular Potential Energy Surfaces [J].
Abbott, Adam S. ;
Turney, Justin M. ;
Zhang, Boyi ;
Smith, Daniel G. A. ;
Altarawy, Doaa ;
Schaefer, Henry F., III .
JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2019, 15 (08) :4386-4398
[3]   Molecular dynamics investigations of the dissociation of SiO2 on an ab initio potential energy surface obtained using neural network methods [J].
Agrawal, PM ;
Raff, LM ;
Hagan, MT ;
Komanduri, R .
JOURNAL OF CHEMICAL PHYSICS, 2006, 124 (13)
[4]   Training Neural Nets To Learn Reactive Potential Energy Surfaces Using Interactive Quantum Chemistry in Virtual Reality [J].
Amabilino, Silvia ;
Bratholm, Lars A. ;
Bennie, Simon J. ;
VaucherM, Alain C. ;
Reiher, Markus ;
Glowacki, David R. .
JOURNAL OF PHYSICAL CHEMISTRY A, 2019, 123 (20) :4486-4499
[5]  
[Anonymous], 2017, LC-GAP: Localized Coulomb Descriptors for the Gaussian Approximation Potential
[6]  
[Anonymous], 2013, MLatom: A Package for Atomistic Simulations with Machine Learning
[7]  
[Anonymous], 1996, THESIS
[8]   Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm [J].
Artrith, Nongnuch ;
Urban, Alexander ;
Ceder, Gerbrand .
JOURNAL OF CHEMICAL PHYSICS, 2018, 148 (24)
[9]   High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide [J].
Artrith, Nongnuch ;
Morawietz, Tobias ;
Behler, Joerg .
PHYSICAL REVIEW B, 2011, 83 (15)
[10]   Prediction of Intramolecular Reorganization Energy Using Machine Learning [J].
Atahan-Evrenk, Sule ;
Atalay, F. Betul .
JOURNAL OF PHYSICAL CHEMISTRY A, 2019, 123 (36) :7855-7863