Chenxing Liang
Graduate Research Assistant
Austin, Texas, United States
Summary
👤
Senior🎓
Top SchoolChenxing Liang is a Ph.D. candidate in Mechanical Engineering at UT Austin with nine years of experience applying molecular dynamics and deep learning to computational materials problems. He builds deep learning-based interatomic potentials (DPMD-kit) and integrates ab initio data with multiscale frameworks to reveal electron-nanofluidic coupling in systems like carbon nanotubes and twisted bilayer graphene. Proficient in Python, C/C++, LAMMPS, VASP/QE, CP2K, TensorFlow, and HPC, he pairs rigorous simulation skills with data-driven modeling and uncertainty analysis. His work spans fundamental nanoscale physics and practical applications such as predicting water distribution in graphene nanochannels and shale gas pipeline reliability. Notably, he developed GPU-accelerated PDE solvers and demonstrated heavy-water behavior in semiconducting CNTs, highlighting a blend of algorithmic efficiency and atomic-scale insight. Fluent in Chinese and English, he thrives in multidisciplinary teams and seeks collaborative projects at the intersection of ML and materials science.
9 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy - PhD, Mechanical Engineering, 3.93/4.0, Doctor of Philosophy - PhD, Mechanical Engineering, 3.93/4.0 at Cockrell School of Engineering, The University of Texas at AustinUndergraduate Exchange Program, Mechanical Engineering, 4.0/4.0, Undergraduate Exchange Program, Mechanical Engineering, 4.0/4.0 at University of MinnesotaBachelor of Engineering - BE, Energy and Power Engineering, 91.79/100 (3.99/4.3), Bachelor of Engineering - BE, Energy and Power Engineering, 91.79/100 (3.99/4.3) at 西安交通大学
chinese and english
Github Skills (1)
counting6
Github contributions (5)
chenxingliang/javascript_for_leetcode
Dec 2019 - Nov 2021
Contributions:101 pushes, 1 branch in 1 year 10 months
CCFLS/FingerPlayWeb
Aug 2016 - Sep 2016
Contributions:10 commits, 9 pushes, 1 branch in 13 days
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