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
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| 2018-06-07 | |
| Source Publication | JOURNAL OF PHYSICAL CHEMISTRY LETTERS
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| ISSN | 1948-7185 |
| Volume | 9Issue:11Pages:2725-2732 |
| Abstract | 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. |
| Subtype | Article |
| WOS Headings | Science & Technology ; Physical Sciences ; Technology |
| DOI | 10.1021/acs.jpclett.8b00684 |
| WOS Keyword | NEURAL-NETWORK POTENTIALS ; QUANTUM MECHANICS/MOLECULAR MECHANICS ; CURVE CROSSING PROBLEMS ; EXCITED-STATE DYNAMICS ; CONICAL INTERSECTIONS ; CLASSICAL DYNAMICS ; CHEMICAL SPACE ; PHOTODYNAMICS ; REPRESENTATION ; TRANSITION |
| Indexed By | SCI |
| Language | 英语 |
| WOS Research Area | Chemistry ; Science & Technology - Other Topics ; Materials Science ; Physics |
| Funding Organization | NSFC(21673266 ; Natural Science Foundation of Shandong Province for Distinguished Young Scholars(JQ201504) ; 21503248) |
| WOS Subject | Chemistry, Physical ; Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary ; Physics, Atomic, Molecular & Chemical |
| WOS ID | WOS:000435026100002 |
| Publisher | AMER CHEMICAL SOC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://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 Author | Lan, Zhenggang |
| Affiliation | 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 |
| 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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