First-principles simulations and machine learning to understand how materials work at the atomic scale — and to design better catalysts for sustainable energy conversion.
Research
Catalysts are the key to clean energy, but designing them remains largely trial-and-error. Our lab bridges this gap by using atomistic simulation to reveal why certain materials catalyze reactions efficiently — and others do not.
Computational Catalyst Design
We explore the vast compositional space of multi-principal-element alloys and nanostructured catalysts. Using DFT, we map how elemental combinations and nanoscale geometry reshape binding energies, surface structures, and catalytic selectivity.
Electrochemical Mechanisms & Theory
We trace reaction pathways on catalyst surfaces to identify active sites and rate-limiting steps. DFT with explicit electric fields, DFT+U, d-band analysis, and microkinetic modeling connect electronic descriptors to performance.
Data-Driven Materials Discovery
Screening thousands of candidates one-by-one is impractical. We combine ML models with first-principles data to accelerate property prediction, develop transferable descriptors, and identify promising catalysts.
Publications
Experience
Education
Teaching
2026 SPRING · FU JEN
- Physical Chemistry I (Quantum)
- Advanced Physical Chemistry (Computational Chemistry)
2025 SPRING · CCU
- Physical Chemistry (EMI)
- Organic Chemistry
- Polymer Physics and Chemistry
- Introduction to Renewable Energy
- Solar Cell Development and Application
2025 FALL · CCU
- Organic Chemistry
- Instrumental Analysis (A & B)
- Intro to Chemical and Materials Engineering
- Introduction to Renewable Energy
2024 FALL · CCU
- Physical Chemistry (EMI)
- Organic Chemistry
- Intro to Chemical and Materials Engineering
- Understanding Green Energy
- Introduction to Energy Sources
Join the Lab
We are looking for curious, motivated students who want to learn computational chemistry from the ground up.
No prior coding or simulation experience required. What matters is genuine curiosity about how materials work and willingness to learn. You will gain hands-on skills in DFT calculations, high-performance computing, data analysis, and scientific writing.
VASP, Materials Studio, Python for data analysis, HPC cluster operation (Slurm), machine learning frameworks for materials screening.
HEA surface catalysis, CO₂RR product selectivity, HER/OER descriptor development, ML-accelerated catalyst screening, electrochemical mechanism modeling.
Undergraduate or graduate students in chemistry, materials science, or chemical engineering interested in computation, modeling, and understanding materials at the atomic level.
Contact
Open to collaborations, student inquiries, and speaking invitations.
Fu Jen Catholic University, Taiwan