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Two-state diabatic potential energy surfaces of ClH2 based on nonadiabatic couplings with neural networks

文献类型：期刊论文

作者Yin, Zhengxi; Guan, Yafu; Fu, Bina1; Zhang, Dong H.1
刊名PHYSICAL CHEMISTRY CHEMICAL PHYSICS
出版日期2019-09-28
卷号21期号:36页码:20372-20383
ISSN号1463-9076
DOI10.1039/c9cp03592c
通讯作者Fu, Bina(bina@dicp.ac.cn) ; Zhang, Dong H.(zhangdh@dicp.ac.cn)
英文摘要A general neural network (NN)-fitting procedure based on nonadiabatic couplings is proposed to generate coupled two-state diabatic potential energy surfaces (PESs) with conical intersections. The elements of the diabatic potential energy matrix (DPEM) can be obtained directly from a combination of the NN outputs in principle. Instead, to achieve higher accuracy, the adiabatic-to-diabatic transformation (ADT) angle (mixing angle) for each geometry is first solved from the NN outputs, followed by individual NN fittings of the three terms of the DPEM, which are calculated from the ab initio adiabatic energies and solved mixing angles. The procedure is applied to construct a new set of two-state diabatic potential energy surfaces of ClH2. The ab initio data including adiabatic energies and derivative couplings are well reproduced. Furthermore, the current diabatization procedure can describe well the vicinity of conical intersections in high potential energy regions, which are located in the T-shaped (C-2v) structure of Cl-H-2. The diabatic quantum dynamical results on diabatic PESs show large differences as compared with the adiabatic results in high collision energy regions, suggesting the significance of nonadiabatic processes in conical intersection regions at high energies.
WOS关键词QUANTUM DYNAMICS ; FEEDFORWARD NETWORKS ; PLUS HD ; STATES ; REPRESENTATIONS ; TRANSFORMATION ; INTERPOLATION ; COLLISIONS ; LEVEL ; TERMS
资助项目National Natural Science Foundation of China[21722307] ; National Natural Science Foundation of China[21673233] ; National Natural Science Foundation of China[21433009] ; National Natural Science Foundation of China[21688102] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB17000000]
WOS研究方向Chemistry ; Physics
语种英语
WOS记录号WOS:000487555400062
出版者ROYAL SOC CHEMISTRY
资助机构National Natural Science Foundation of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://cas-ir.dicp.ac.cn/handle/321008/172744]  
专题大连化学物理研究所_中国科学院大连化学物理研究所
通讯作者Fu, Bina; Zhang, Dong H.
作者单位1.Chinese Acad Sci, State Key Lab Mol React Dynam, Dalian Inst Chem Phys, Dalian 116023, Peoples R China
2.Chinese Acad Sci, Ctr Theoret & Computat Chem, Dalian Inst Chem Phys, Dalian 116023, Peoples R China
推荐引用方式
GB/T 7714		 Yin, Zhengxi,Guan, Yafu,Fu, Bina,et al. Two-state diabatic potential energy surfaces of ClH2 based on nonadiabatic couplings with neural networks[J]. PHYSICAL CHEMISTRY CHEMICAL PHYSICS,2019,21(36):20372-20383.
APA Yin, Zhengxi,Guan, Yafu,Fu, Bina,&Zhang, Dong H..(2019).Two-state diabatic potential energy surfaces of ClH2 based on nonadiabatic couplings with neural networks.PHYSICAL CHEMISTRY CHEMICAL PHYSICS,21(36),20372-20383.
MLA Yin, Zhengxi,et al."Two-state diabatic potential energy surfaces of ClH2 based on nonadiabatic couplings with neural networks".PHYSICAL CHEMISTRY CHEMICAL PHYSICS 21.36(2019):20372-20383.

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来源：大连化学物理研究所

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