QIBEBT-IR
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
Source PublicationJOURNAL OF PHYSICAL CHEMISTRY LETTERS
ISSN1948-7185
Volume9Issue:11Pages:2725-2732
AbstractWe 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.
SubtypeArticle
WOS HeadingsScience & Technology ; Physical Sciences ; Technology
DOI10.1021/acs.jpclett.8b00684
WOS KeywordNEURAL-NETWORK POTENTIALS ; QUANTUM MECHANICS/MOLECULAR MECHANICS ; CURVE CROSSING PROBLEMS ; EXCITED-STATE DYNAMICS ; CONICAL INTERSECTIONS ; CLASSICAL DYNAMICS ; CHEMICAL SPACE ; PHOTODYNAMICS ; REPRESENTATION ; TRANSITION
Indexed BySCI
Language英语
WOS Research AreaChemistry ; Science & Technology - Other Topics ; Materials Science ; Physics
Funding OrganizationNSFC(21673266 ; Natural Science Foundation of Shandong Province for Distinguished Young Scholars(JQ201504) ; 21503248)
WOS SubjectChemistry, Physical ; Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary ; Physics, Atomic, Molecular & Chemical
WOS IDWOS:000435026100002
PublisherAMER CHEMICAL SOC
Citation statistics
Cited Times:23[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.qibebt.ac.cn/handle/337004/11386
Collection中国科学院青岛生物能源与过程研究所
Corresponding AuthorLan, Zhenggang
Affiliation1.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
Recommended Citation
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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