Shiyf's Blog

About Myself

史云飞

Yun-Fei, Shi

PhD. student, Physical Chemistry, Department of Chemistry, Fudan University

Email: yfshi19@fudan.edu.cn

Phone: +86 13818897381

Education:

  • 2015 ~ 2019: Master Degree in Material Chemistry, Department of Materials Science, Fudan University
  • since 2019: PhD in Physical Chemistry, Department of Chemistry, Fudan University

Awards

  • Fudan University Qushiduxing Scholarship for the 2021-2022 academic year

Research Interests

  • First-principle computational simulation of heterogeneous catalysis
  • Automated reaction sampling
  • Development of machine-learning potential energy surface

Project

  • LASPView
    • Atomic structure visualization tool. Based on the Unity3D, it supports basic structure rendering and structure editing, and can communicate with remote Linux servers to send and receive structures.
  • Microkinetics-Guided Machine Learning Pathway Search (MMLPS)
    • Utilize the Stochastic Surface Walking (SSW) and neural network potential (NN) to automatically sample elementary reactions, analyze low barrier reaction pathway for multi-step reactions, and perform microkinetics simulations.
  • CUDA accelerated PTSD structure descriptor calculation
    • Accelerate the training and inference of machine-learning potential with GPUs

Skill

  • Proficient in python programming and common libraries, understand various other programming languages(C#, C++, fortran, bash, etc.)
  • Maintain Linux server and build computing clusters (NTFS, ypbind, SGE, etc.)
  • Understand basic deep learning algorithms. Understand the CUDA programming model.

Publication

  1. Pei-Lin Kang, Yun-Fei Shi, Cheng Shang, and Zhi-Pan Liu*, Artificial Intelligence Pathway Search to Resolve Catalytic Glycerol Hydrogenolysis Selectivity. Chem. Sci. 2022, 13, 27, 8148–8160, https://doi.org/10.1039/D2SC02107B
  2. Yun-Fei Shi, Pei-Lin Kang, Cheng Shang*, and Zhi-Pan Liu*, Methanol Synthesis from CO2/CO Mixture on Cu–Zn Catalysts from Microkinetics-Guided Machine Learning Pathway Search, J. Am. Chem. Soc. 2022, 144, 29, 13401–13414, https://doi.org/10.1021/jacs.2c06044
  3. Yun-Fei Shi#, Zheng-Xin Yang#, Pei-Lin Kang, Cheng Shang, P. Hu*, Zhi-Pan Liu*, Machine Learning for Chemistry: Basics and Applications, Engineering 2023, in press, https://doi.org/10.1016/j.eng.2023.04.013
  4. Yun-Fei Shi, Sicong Ma*, Zhi-Pan Liu*, Copper-based Catalysts for CO2 Hydrogenation: A Perspective on the Active Site, EES Catal. 2023, in press, https://doi.org/10.1039/D3EY00152K