Representation of coupled adiabatic potential energy surfaces using neural network based quasi-diabatic Hamiltonians: 1,2 (2)A ' states of LiFH
文献类型：期刊论文
| 作者 | Guan, Yafu2; Zhang, Dong H.1,3; Guo, Hua4; Yarkony, David R.2 |
| 刊名 | PHYSICAL CHEMISTRY CHEMICAL PHYSICS
 |
| 出版日期 | 2019-07-14 |
| 卷号 | 21期号:26页码:14205-14213 |
| ISSN号 | 1463-9076 |
| DOI | 10.1039/c8cp06598e |
| 通讯作者 | Guan, Yafu(yguan15@jhu.edu) |
| 英文摘要 | An analytic quasi-diabatic representation of ab initio electronic structure data is key to the accurate quantum mechanical description of non-adiabatic chemical processes. In this work, a general neural network (NN) fitting procedure is proposed to generate coupled quasi-diabatic Hamiltonians (H-d) that are capable of representing adiabatic energies, energy gradients, and derivative couplings over a wide range of geometries. The quasi-diabatic representation for LiFH is used as a testing example. The fitting data including adiabatic energies, energy gradients and interstate couplings are obtained from a previously fitted analytical quasi-diabatic potential energy matrix, and are well reproduced by the NN fitting. Most importantly, the NN fitting also yields quantum dynamic results that reproduce those on the original LiFH diabatic Hamiltonian, demonstrating the ability of NN to generate highly accurate quasi-diabatic Hamiltonians. |
| WOS关键词 | QUANTUM DYNAMICS ; CONICAL INTERSECTIONS ; FEEDFORWARD NETWORKS ; SCATTERING ; CHEMISTRY ; RIDGES |
| 资助项目 | Department of Energy[DE-SC0015997] ; National Natural Science Foundation of China[21433009] ; National Natural Science Foundation of China[21590804] ; National Natural Science Foundation of China[21688102] |
| WOS研究方向 | Chemistry ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000474136100031 |
| 出版者 | ROYAL SOC CHEMISTRY |
| 资助机构 | Department of Energy ; Department of Energy ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Department of Energy ; Department of Energy ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Department of Energy ; Department of Energy ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Department of Energy ; Department of Energy ; National Natural Science Foundation of China ; National Natural Science Foundation of China |
| 源URL | [http://cas-ir.dicp.ac.cn/handle/321008/175124]  |
| 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 | Guan, Yafu |
| 作者单位 | 1.Chinese Acad Sci, Dalian Inst Chem Phys, Ctr Theoret Computat Chem, Dalian 116023, Peoples R China 2.Johns Hopkins Univ, Dept Chem, Charles & 34Th St, Baltimore, MD 21218 USA 3.Chinese Acad Sci, Dalian Inst Chem Phys, State Key Lab Mol React Dynam, Dalian 116023, Peoples R China 4.Univ New Mexico, Dept Chem & Chem Biol, Albuquerque, NM 87131 USA |
| 推荐引用方式 GB/T 7714 | Guan, Yafu,Zhang, Dong H.,Guo, Hua,et al. Representation of coupled adiabatic potential energy surfaces using neural network based quasi-diabatic Hamiltonians: 1,2 (2)A ' states of LiFH[J]. PHYSICAL CHEMISTRY CHEMICAL PHYSICS,2019,21(26):14205-14213. |
| APA | Guan, Yafu,Zhang, Dong H.,Guo, Hua,&Yarkony, David R..(2019).Representation of coupled adiabatic potential energy surfaces using neural network based quasi-diabatic Hamiltonians: 1,2 (2)A ' states of LiFH.PHYSICAL CHEMISTRY CHEMICAL PHYSICS,21(26),14205-14213. |
| MLA | Guan, Yafu,et al."Representation of coupled adiabatic potential energy surfaces using neural network based quasi-diabatic Hamiltonians: 1,2 (2)A ' states of LiFH".PHYSICAL CHEMISTRY CHEMICAL PHYSICS 21.26(2019):14205-14213. |
入库方式： OAI收割
来源：大连化学物理研究所
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