About
PhD Candidate in Materials Chemistry at TU Wien. My research focuses on computational thermal transport — using first-principles methods, molecular dynamics, and machine-learning interatomic potentials to understand heat conduction in nanomaterials. I am part of the PULGON project, studying phonons in quasi-1D systems via line group symmetry.
Education
PhD in Materials Chemistry
2023-07-12
TU Wien
MS in Theoretical Physics
2020-09-01
2023-07-01
South China University of Technology
BS in Physics
2016-09-01
2020-07-01
South China University of Technology
📚 My Research
My research focuses on the intersection of materials science and computational physics. I specialize in using first-principles calculations to investigate the fundamental properties of materials.
My key areas of interest include:
- Heat Transport: Understanding and predicting thermal properties in complex and quasi-1D systems.
- Phonon Dynamics: Studying lattice vibrations and their behavior in nanomaterials, particularly through symmetry-adapted approaches as part of the PULGON project.
- Machine Learning: Applying machine learning techniques to accelerate and enhance materials discovery and property prediction.
I am always open to new collaborations and discussions. Please feel free to reach out.
Featured Publications
Recent Publications
(2026).
Ab-initio heat transport in defect-laden quasi-1D systems from a symmetry-adapted perspective.
npj Comput. Mater..
(2025).
Accelerating first-principles molecular-dynamics thermal conductivity calculations for complex systems.
J. Chem. Theory Comput..
(2025).
Accelerating Green-Kubo heat transport simulations for quasi-1D systems using highly accurate machine-learned force fields.
PSI-K 2025: abstracts book.
(2025).
Accelerating the identification of stable configurations in mixed-anion perovskite materials.
Comput. Mater. Sci..
(2024).
Residual entropy of ice: A study based on transfer matrices.
Phys. Rev. B.
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