华南师范大学

报告人简介

Zhenggang Lan is a Professor at South China Normal University, Guangzhou, China. He received his BS in Chemical Physics from the University of Science and Technology of China in 2000 and his MS in Theoretical Chemistry from the Institute of Chemistry, Chinese Academy of Sciences (CAS), in 2003. He obtained his PhD in Theoretical Chemistry from the Technical University of Munich, Germany in 2007. Afterwards he worked as a postdoctoral researcher and the research Scientist at Technical University of Munich and the Max-Planck-Institut für Kohlenforschung. He then became a Full Professor at the Qingdao Institute of Bioenergy and Bioprocess Technology, CAS, before joining South China Normal University in 2018.

His current research interests focus on theoretical and computational chemistry, photophysics, photochemistry, nonadiabatic dynamics, molecular excited states, and quantum dynamics simulations. He has authored over 140 peer-reviewed publications in prestigious journals including Nature Communications, JACS, Angew. Chem. Int. Edit., J. Chem. Theory Comput., and so on. He has been an invited speaker at international conferences on more than 15 occasions.

报告摘要

Nonadiabatic dynamics widely exist in photophysics, photochemistry and photobiology. We tried to develop theoretical approaches to study the photoinduced nonadiabatic dynamics. A few topics will be discussed. We combined the doorway-window representation of the nonlinear response theories and ab initio nonadiabatic dynamics to simulate the time-resolved pump-probe spectra, including both transient absorption spectra and time-resolved fluorescence spectra. Two interesting examples, including photoinduced energy transfer and photoisomerization, are discussed.

We tried to combine deep leaning method and numerical accurate quantum dynamics approach to simulate the long-time quantum evolution of open quantum system. This approach allows us to obtain the evolution of reduced density matrix of open quantum system with a low computational cost. It demonstrates that the deep learning approach is the important tool to speed up the long-time quantum evolution. The similar time-series analysis tool can also be used to propagate all nuclear and electronic degrees of freedom in the trajectory evolution of the SQC-MM dynamics.

报告地点：中国科学院物理研究所怀柔园区X1楼101会议室

腾讯视频会议号：733-320-717，会议密码：250904

邀请人：张鹏举 特聘研究员

联系人：万   源　 研究员

           汪非凡 　副研究员

　　　　田春璐  cltian@iphy.ac.cn

主办方：中国科学院物理研究所、松山湖材料实验室