电子邮箱：wanghaosd@sdu.edu.cn

教育及工作经历：

研究员（山东大学，2021.09至今）

博士后（美国得克萨斯大学奥斯汀分校，2018.10-2021.08）

博士（美国杜克大学，2013.08-2018.09）

学士（山东大学，2009.09-2013.06）

研究方向：

生物大分子多尺度模拟，稀有事件模拟，科学机器学习。

个人主页（http://www.qitcs.qd.sdu.edu.cn/info/1034/1274.htm）

代表性论文：

1）X. Ji, R. Wang, H. Wang*, and W. Liu, On committor functions in Milestoning, J. Chem. Phys. 2023, 159, 244115.

2) R. Wang, H. Wang*, W. Liu, and R. Elber, Approximating first hitting point distribution in Milestoning for rare event kinetics, J. Chem. Theory Comput. 2023, 19, 19, 6816-6826.

3) Y. Chen, H. Li, B. Hou, A. Wu, W. Wu, C. Li*, H. Wang*, D. Chen,* and X. Wang*, NaYF₄: Yb/Er@Mn₃O₄@GOX nanocomposite for upconversion fluorescence imaging and synergistic cascade cancer therapy by apoptosis and ferroptosis, Small,  2023, 20, 1, 2304438.

4) L. Ye, H. Wang*, Y. Zhang, Y. Xiao, and W. Liu*, Real-time time-dependent density functional theories with large time step and short simulation time, Comprehenseive Computational Chemistry (book chapter), 2023, Elsevier.

5) A. Cardenas, A. Hunter, H. Wang, and R. Elber*, ScMiles2: A script to conduct and analyze Milestoning trajectories for long time dynamcis, 2022, J. Chem. Theory Comput. 18, 11, 6952-6965.

6) L. Ye, H. Wang*, Y. Zhang, and W. Liu*, Self-adaptive real-time time-dependent density functional theory for core excitations, 2022, 157, 074106.

7) R. Elber*, A. Fathizadeh, P. Ma, and H. Wang, Modeling molecular kinetics with Milestoning, WIREs: Comput. Mol. Sci. 2021, 11, 4, e1512.

8) H. Wang and R. Elber*, Catalytic magnesium as a door stop for DNA sliding, J. Phys. Chem. B 2021, 125, 14, 3494-3500.

9) H. Wang and R. Elber*, Milestoning with wind: Exploring the impact of a biasing potential in exact calculation of kineitcs, J. Chem. Phys. 2020, 152, 224105.

10) H. Wang, N. Huang, T. Dangerfield, K. Johnson, J. Gao, and R. Elber*, Exploring the reaction mechanism of HIV reverse transcriptase with a nulceotide substrate, J. Phys. Chem. B 2020, 124, 21, 4270-4283.

11) H. Wang and W. Yang*, Toward building protein force feilds by residue-based systematic molecular fragmentation adn neural network, J. Chem. Theory Comput. 2019, 15, 2, 1409-1417.

12) H. Wang and W. Yang*, Force field for water based on neural network, J. Phys. Chem. Lett. 2018, 9, 12, 3232-3240.  

13) H. Wang and W. Yang*, Determining polarizable force fields with electrostatic potentials from quantum mechanical linear response theory, J. Chem. Phys. 2016, 144, 224107.