KMS Qingdao Institute of Biomass Energy and Bioprocess Technology ,CAS
Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation | |
Hu, Deping1,2; Xie, Yu1; Li, Xusong1,2; Li, Lingyue2; Lan, Zhenggang1,2 | |
2018-06-07 | |
发表期刊 | JOURNAL OF PHYSICAL CHEMISTRY LETTERS |
ISSN | 1948-7185 |
卷号 | 9期号:11页码:2725-2732 |
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. |
文章类型 | Article |
WOS标题词 | Science & Technology ; Physical Sciences ; Technology |
DOI | 10.1021/acs.jpclett.8b00684 |
关键词[WOS] | NEURAL-NETWORK POTENTIALS ; QUANTUM MECHANICS/MOLECULAR MECHANICS ; CURVE CROSSING PROBLEMS ; EXCITED-STATE DYNAMICS ; CONICAL INTERSECTIONS ; CLASSICAL DYNAMICS ; CHEMICAL SPACE ; PHOTODYNAMICS ; REPRESENTATION ; TRANSITION |
收录类别 | SCI |
语种 | 英语 |
WOS研究方向 | Chemistry ; Science & Technology - Other Topics ; Materials Science ; Physics |
项目资助者 | NSFC(21673266 ; Natural Science Foundation of Shandong Province for Distinguished Young Scholars(JQ201504) ; 21503248) |
WOS类目 | Chemistry, Physical ; Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary ; Physics, Atomic, Molecular & Chemical |
WOS记录号 | WOS:000435026100002 |
出版者 | AMER CHEMICAL SOC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.qibebt.ac.cn/handle/337004/11386 |
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. |
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