Fengyu Xie
Postdoctoral Research Fellow at University of Science and Technology of China
Suzhou City, Jiangsu, China
Summary
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Senior🎓
Top SchoolFengyu Xie is a computational materials scientist and postdoctoral researcher with nine years of experience applying machine learning and first-principles methods to predict thermodynamic and kinetic properties of crystalline solids, with a focus on Li-ion battery materials. He developed novel regularization approaches for cluster expansion Hamiltonians and built an automation package for lattice thermodynamics integrated with Pymatgen and Atomate, introducing new sampling methods for ionic systems. His work includes a framework to model equilibrium voltage profiles and oxygen redox in disordered rock-salt cathodes and a first-principles thermodynamic analysis of anode-less Ag/C battery designs that informed optimization strategies. Trained at UC Berkeley and experienced at Berkeley Lab and in industry at Huawei, he blends rigorous theory, software engineering, and practical battery insights. Outside the lab he pursues astrophotography and explores complex systems through sandbox simulation games, reflecting a cross-disciplinary curiosity that informs his research creativity.
9 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Materials Science and Engineering, Doctor of Philosophy - PhD, Materials Science and Engineering at University of California, BerkeleyBachelor's degree, Chemistry, GPA 3.76, Bachelor's degree, Chemistry, GPA 3.76 at Peking University
Github Skills (43)
fenics10
materials-informatics10
ising-model10
mechanics10
thermodynamics10
monte-carlo10
statistical-mechanics10
computational-physics9
dft9
density-functional-theory9
linear-regression9
sampling9
cross-validation9
bioinformatics9
classification-algorithm9
Programming languages (3)
Github contributions (5)
qchempku2017/smol
May 2022 - Feb 2025
Statistical Mechanics on Lattices
Contributions:223 pushes, 42 branches in 2 years 10 months
statisticallatticescosmologymonte-carlomechanics
qchempku2017/sparse-lm
Jun 2022 - Sep 2023
Sparse Linear Regression Models
Contributions:63 pushes, 4 branches in 1 year 3 months
regressionregression-modelslinear-regressionsparsemachine-learning
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