My Work
Interpretable Descriptors for Hydrogen-Based Superconductors
Constructed interpretable SISSO descriptor models to predict the critical temperature of hydrogen-based superconductors at moderate pressures. Published in Materials Today Physics (2026, 63, 102073). The prediction formulas have been packaged into a Python tool and an online calculator.
- Python
- SISSO
- VASP
- Superconductors
USPEX Analyzer
A browser-based interactive analysis platform for USPEX crystal structure prediction results. Features include convex hull visualization, Pareto front analysis, scatter plot explorer with GIF export, genealogy tracking, and flexible data filtering and export.
- React
- TypeScript
- Plotly.js
- D3.js
- Tailwind CSS
About Me
I am a M.S. candidate in Materials Physics and Chemistry at Guangdong University of Technology. My research focuses on crystal structure prediction, machine learning-assisted materials design, and first-principles calculations.
My Resume
