陈文恺 博士，副教授，硕士生导师

电子邮件：wenkaichen@hebtu.edu.cn

科研室：理科群7号楼B-404

欢迎对结构化学、物理化学、理论计算化学、光化学、机器学习、程序编写等方面感兴趣的同学报考本组化学专业、材料与化工专业的研究生！

学习经历：

2013.09-2017.06 北京师范大学 理学学士

2017.09-2022.06 北京师范大学 理学博士

工作经历：

2022.07-至今 河北师范大学 历任讲师、副教授

主讲课程：

《结构化学》、《计算化学实验》

研究领域：

理论及计算光化学。具体的研究方向为：（1）基于低标度电子结构方法和机器学习技术的非绝热动力学模拟方案；（2）分子体系和周期性体系光物理过程的理论研究；（3）基于大数据和机器学习探索催化剂与催化活性间的构效关系。

科研项目：

国家自然科学基金青年科学基金(22303025)

河北省自然科学基金青年科学基金(B2023205003)

河北省高等学校科学技术研究项目-青年基金项目(QN2023176)

河北师范大学科技类博士基金(L2023B12)

代表性论著：

(1)Wen-Kai Chen, Xiang-Yang Liu, Wei-Hai Fang, Pavlo O. Dral, Ganglong Cui*;Deep Learning for Nonadiabatic Excited-State Dynamics, J. Phys. Chem. Lett.,2018, 9, 6702-6708.

(2)Wen-Kai Chen, Wei-Hai Fang, Ganglong Cui*; IntegratingMachine Learning with the Multilayer Energy-Based Fragment Method for ExcitedStates of Large Systems, J. Phys. Chem. Lett., 2019,10, 7836-7841.

(3)Wen-Kai Chen, Wei-Hai Fang, Ganglong Cui*; A Multi-LayerEnergy-Based Fragment Method for Excited States and Nonadiabatic Dynamics, Phys.Chem. Chem. Phys., 2019, 21, 22695-22699.

(4)Wen-Kai Chen, Yaolong Zhang, Bin Jiang, Wei-Hai Fang, Ganglong Cui*;Efficient Construction of Excited-State Hessian Matrices with Machine LearningAccelerated Multilayer Energy-Based Fragment Method, J. Phys. Chem. A, 2020,124, 5684-5695.

(5)Wen-Kai Chen, Wei-Hai Fang, Ganglong Cui*; ExtendingMulti-Layer Energy-Based Fragment Method for Excited-State Calculations ofLarge Covalently Bonded Fragment Systems, J. Chem. Phys., 2023,158, 044110.

(6)Wen-Kai Chen, Sheng-Rui Wang, Xiang-Yang Liu, Wei-Hai Fang, Ganglong Cui*;Nonadiabatic Derivative Couplings Calculated Using Information of PotentialEnergy Surfaces without Wavefunctions: Ab Initio and Machine LearningImplementations, Molecules, 2023, 28, 4222.

(7)Yanjiang Wang, Chang Zhao, Wen-Kai Chen*, Yanli Zeng*;Chalcogen Bond Catalysis with Telluronium Cations for Bromination Reaction:Importance of Electrostatic and Polarization Effects, Chem. Eur. J., 2023,29, e202302749.

(8) Wen-Kai Chen, Xiang-Yang Liu,Ganglong Cui*; Generalized Trajectory-Based Surface-Hopping (GTSH)Nonadiabatic Dynamics with Time-Dependent Density Functional Theory:Methodologies and Applications. In Time-Dependent Density Functional Theory;Jenny Stanford Publishing: New York, 2022; pp 199–250. (书籍章节)