Xi Tan

Xi Tan

M.S., School of Energy and Power Engineering, Huazhong University of Science and Technology

Curriculum Vitae

Education

  • B.S. in Nanjing Normal University (NNU), September 2018 – June 2022
  • M.S. in Huazhong University of Science and Technology (HUST), September 2022 – Present

Experience

  • October 2021 – Present
    • Research Assistant in Huazhong University of Science and Technology
    • Mentor: Prof. Guang Feng
  • September 2018 – June 2022
    • Research Assistant in Nanjing Normal University
    • Mentor: Prof. Jing Qi

Selected Publications

Google Scholar personal academic profile

  1. Xi Tan, Ming Chen, Jinkai Zhang, Shiqi Li, Huajie Zhang, Long Yang, Tian Sun, Xin Qian*, Guang Feng*. Decoding electrochemical processes of lithium-ion batteries by classical molecular dynamics simulations, Advanced Energy Materials, 2024, 14, 2400564. Linkage

  2. Liang Zeng, Xi Tan, Xiangyu Ji, Shiqi Li, Jinkai Zhang, Jiaxing Peng, Sheng Bi, Guang Feng*. Constant charge method or constant potential method: Which is better for molecular modeling of electrical double layers? Journal of Energy Chemistry, 2024, 94, 54. Linkage

  3. Liang Zeng, Xi Tan, Nan Huang, Guang Feng*. Progress on understanding heat generation of electrical double layers, Current Opinion in Electrochemistry, 2024, 46, 101503. Linkage

Skills:

  • Molecular Modeling Skills:
    • LAMMPS: Conducted ReaxFF simulation research on oxidation corrosion of supercritical water in iron pipelines during undergraduate studies, with a bachelor’s graduation project focused on the mechanism of ReaxFF pseudocapacitor supercapacitors under a constant potential. Presently, conducting molecular simulation research on supercapacitors under a constant potential.
    • GROMACS: Performed potentiostatic empirical potential molecular dynamics simulations on electrochemical solid-liquid interfaces, including Li and Zn aqueous batteries and supercapacitors.
    • GPUMD/DeePMD: Undertook machine learning potential training for the reaction at the electrochemical solid-liquid interface and molecular dynamics simulation research on related chemical reaction mechanisms. (For instance, the pseudocapacitive supercapacitor composed of KOH solution and NiHAB MOF).
    • VASP/CP2K: Furnished AIMD and DFT static calculations essential for training machine learning potentials.