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Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation

文献类型：期刊论文

作者Hu, Deping1,2; Xie, Yu1; Li, Xusong1,2; Li, Lingyue2; Lan, Zhenggang1,2
刊名JOURNAL OF PHYSICAL CHEMISTRY LETTERS
出版日期2018-06-07
卷号9期号:11页码:2725-2732
ISSN号1948-7185
DOI10.1021/acs.jpclett.8b00684
文献子类Article
英文摘要We discuss a theoretical approach that employs machine learning potential energy surfaces (ML-PESs) in the nonadiabatic dynamics simulation of polyatomic systems by taking 6-aminopyrimidine as a typical example. The Zhu-Nakamura theory is employed in the surface hopping dynamics, which does not require the calculation of the nonadiabatic coupling vectors. The kernel ridge regression is used in the construction of the adiabatic PESs. In the nonadiabatic dynamics simulation, we use ML-PESs for most geometries and switch back to the electronic structure calculations for a few geometries either near the S-1/S-0 conical intersections or in the out-of-confidence regions. The dynamics results based on ML-PESs are consistent with those based on CASSCF PESs. The ML-PESs are further used to achieve the highly efficient massive dynamics simulations with a large number of trajectories. This work displays the powerful role of ML methods in the nonadiabatic dynamics simulation of polyatomic systems.
WOS关键词NEURAL-NETWORK POTENTIALS ; QUANTUM MECHANICS/MOLECULAR MECHANICS ; CURVE CROSSING PROBLEMS ; EXCITED-STATE DYNAMICS ; CONICAL INTERSECTIONS ; CLASSICAL DYNAMICS ; CHEMICAL SPACE ; PHOTODYNAMICS ; REPRESENTATION ; TRANSITION
WOS研究方向Chemistry ; Science & Technology - Other Topics ; Materials Science ; Physics
语种英语
WOS记录号WOS:000435026100002
出版者AMER CHEMICAL SOC
资助机构NSFC(21673266 ; Natural Science Foundation of Shandong Province for Distinguished Young Scholars(JQ201504) ; 21503248)
源URL[http://ir.qibebt.ac.cn/handle/337004/11385]  
专题中国科学院青岛生物能源与过程研究所
通讯作者Lan, Zhenggang
作者单位1.Chinese Acad Sci, Qingdao Inst Bioenergy & Bioproc Technol, CAS Key Lab Biobased Mat, Qingdao 266101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Hu, Deping,Xie, Yu,Li, Xusong,et al. Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation[J]. JOURNAL OF PHYSICAL CHEMISTRY LETTERS,2018,9(11):2725-2732.
APA Hu, Deping,Xie, Yu,Li, Xusong,Li, Lingyue,&Lan, Zhenggang.(2018).Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation.JOURNAL OF PHYSICAL CHEMISTRY LETTERS,9(11),2725-2732.
MLA Hu, Deping,et al."Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation".JOURNAL OF PHYSICAL CHEMISTRY LETTERS 9.11(2018):2725-2732.

入库方式： OAI收割

来源：青岛生物能源与过程研究所

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