Computational Chemistry for Energy Catalysis & High-Entropy Materials
We use first-principles simulations and machine learning to understand how materials work at the atomic scale, and to design better catalysts for sustainable energy conversion.
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.
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 in HEAs, Fe–Cu, Cu–Ru, and Cu–Pd bimetallic clusters.
We trace reaction pathways on catalyst surfaces to identify active sites and rate-limiting steps for energy-relevant reactions. Our toolkit includes DFT with explicit electric fields, DFT+U corrections, d-band analysis, and microkinetic modeling—connecting electronic descriptors directly to catalytic performance.
Screening thousands of candidate materials one-by-one is impractical. We combine machine learning models with first-principles data to accelerate property prediction, develop transferable descriptors, and identify promising catalysts from vast compositional spaces.
Chen-Cheng Liao in bold denotes first or co-first authorship. Full list available on Google Scholar.
We are looking for curious, motivated students who want to learn computational chemistry from the ground up.
No prior coding or simulation experience is 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 who are interested in computation, modeling, and understanding materials at the atomic level
Open to collaborations, student inquiries, and speaking invitations.