以透热表象为基础的激发态分子反应动力学

（1）以神经网络为基础的透热化方法

（2）单分子的光解

（3）分子碰撞中的非绝热效应

（3）系间窜越的研究

（4）分子与外场的相互作用

本课题组提供宽松的学术和生活氛围、较多的国际和国内交流机会，热烈欢迎对理论化学有兴趣的学生加入我们（硕士、博士、硕博连读）。

070304-物理化学

分子反应动力学
非绝热动力学

2013-09--2018-06   中国科学院大学   理学博士
2008-09--2013-07   北京大学   理学学士

   

2022-01~现在, 中国科学院大连化学物理研究所, 研究员
2021-09~2022-01,中国科学院大连化学物理研究所, 副研究员
2018-08~2021-08,约翰霍普金斯大学, 博士后

   

[1] 官亚夫, David R. Yarkony, 张东辉. Permutation invariant polynomial neural network based diabatic ansatz for the (E + A) × (e + a) Jahn–Teller and Pseudo-Jahn–Teller systems. JOURNAL OF CHEMICAL PHYSICS[J]. 2022, 157(1): 014110-014110, [2] Guan, Yafu, Xie, Changjian, Guo, Hua, Yarkony, David R. Enabling a Unified Description of Both Internal Conversion and Intersystem Crossing in Formaldehyde: A Global Coupled Quasi-Diabatic Hamiltonian for Its S0, S1, and T1 States. JOURNAL OF CHEMICAL THEORY AND COMPUTATION[J]. 2021, 17(7): 4157-4168, http://dx.doi.org/10.1021/acs.jctc.1c00370.
[3] Guan, Yafu, Xie, Changjian, Yarkony, David R, Guo, Hua. High-fidelity first principles nonadiabaticity: diabatization, analytic representation of global diabatic potential energy matrices, and quantum dynamics. PHYSICAL CHEMISTRY CHEMICAL PHYSICS[J]. 2021, 23(44): 24962-24983, [4] Yin, Zhengxi, Braams, Bastiaan J, Guan, Yafu, Fu, Bina, Zhang, Dong H. A fundamental invariant-neural network representation of quasi-diabatic Hamiltonians for the two lowest states of H-3. PHYSICAL CHEMISTRY CHEMICAL PHYSICS[J]. 2021, 23(2): 1082-1091, http://dx.doi.org/10.1039/d0cp05047d.
[5] Guan, Yafu, Xie, Changjian, Guo, Hua, Yarkony, David R. Neural Network Based Quasi-diabatic Representation for S0 and S1 States of Formaldehyde. JOURNAL OF PHYSICAL CHEMISTRY A[J]. 2020, 124(49): 10132-10142, https://www.webofscience.com/wos/woscc/full-record/WOS:000599586100005.
[6] Guan, Yafu, Guo, Hua, Yarkony, David R. Extending the Representation of Multistate Coupled Potential Energy Surfaces To Include Properties Operators Using Neural Networks: Application to the 1,2(1)A States of Ammonia. JOURNAL OF CHEMICAL THEORY AND COMPUTATION[J]. 2020, 16(1): 302-313, https://www.webofscience.com/wos/woscc/full-record/WOS:000508474800025.
[7] Guan, Yafu, Yarkony, David R. Accurate Neural Network Representation of the Ab Initio Determined Spin-Orbit Interaction in the Diabatic Representation Including the Effects of Conical Intersections. JOURNAL OF PHYSICAL CHEMISTRY LETTERS[J]. 2020, 11(5): 1848-1858, https://www.webofscience.com/wos/woscc/full-record/WOS:000518706000036.
[8] Hong, Yingyue, Yin, Zhengxi, Guan, Yafu, Zhang, Zhaojun, Fu, Bina, Zhang, Dong H. Exclusive Neural Network Representation of the Quasi-Diabatic Hamiltonians Including Conical Intersections. JOURNAL OF PHYSICAL CHEMISTRY LETTERS[J]. 2020, 11(18): 7552-7558, https://www.webofscience.com/wos/woscc/full-record/WOS:000574906500013.
[9] Guan, Yafu, Zhang, Dong H, Guo, Hua, Yarkony, David R. 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[J]. 2019, 21(26): 14205-14213, https://www.webofscience.com/wos/woscc/full-record/WOS:000474136100031.
[10] Yin, Zhengxi, Guan, Yafu, Fu, Bina, Zhang, Dong H. Two-state diabatic potential energy surfaces of ClH2 based on nonadiabatic couplings with neural networks. PHYSICAL CHEMISTRY CHEMICAL PHYSICS[J]. 2019, 21(36): 20372-20383, https://www.webofscience.com/wos/woscc/full-record/WOS:000487555400062.
[11] Guan, Yafu, Guo, Hua, Yarkony, David R. Neural network based quasi-diabatic Hamiltonians with symmetry adaptation and a correct description of conical intersections. JOURNAL OF CHEMICAL PHYSICS[J]. 2019, 150(21): https://www.webofscience.com/wos/woscc/full-record/WOS:000470725300036.
[12] Yuan, Daofu, Guan, Yafu, Chen, Wentao, Zhao, Hailin, Yu, Shengrui, Luo, Chang, Tan, Yuxin, Xie, Ting, Wang, Xingan, Sun, Zhigang, Zhang, Dong H, Yang, Xueming. Observation of the geometric phase effect in the H plus HD -> H-2 + D reaction. SCIENCE[J]. 2018, 362(6420): 1289-+, http://cas-ir.dicp.ac.cn/handle/321008/166410.
[13] Guan, Yafu, Yang, Shuo, Zhang, Dong H. Application of Clustering Algorithms to Partitioning Configuration Space in Fitting Reactive Potential Energy Surfaces. JOURNAL OF PHYSICAL CHEMISTRY A[J]. 2018, 122(12): 3140-3147, http://cas-ir.dicp.ac.cn/handle/321008/169050.
[14] Guan, Yafu, Yang, Shuo, Zhang, Dong H. Construction of reactive potential energy surfaces with Gaussian process regression: active data selection. MOLECULAR PHYSICS[J]. 2018, 116(7-8): 823-834, http://cas-ir.dicp.ac.cn/handle/321008/169062.
[15] He, Shan, Guan, Yafu, Liu, Dong, Xia, Xusheng, Gai, Baodong, Hu, Shu, Guo, Jingwei, Sang, Fengting, Jin, Yuqi. Energy-Transfer Kinetics Driven by Midinfrared Amplified Spontaneous Emission after Two-Photon Excitation from Xe (s(0)) to the Xe (6p1/2(0)) State. JOURNAL OF PHYSICAL CHEMISTRY A[J]. 2017, 121(18): 3430-3436, https://www.webofscience.com/wos/woscc/full-record/WOS:000401402800010.
[16] Guan, Yafu, Fu, Bina, Zhang, Dong H. Construction of diabatic energy surfaces for LiFH with artificial neural networks. JOURNAL OF CHEMICAL PHYSICS[J]. 2017, 147(22): http://cas-ir.dicp.ac.cn/handle/321008/168408.