Shih-Han Wang

Chemical Engineer

Talent

Blacksburg, MontgomeryMember since March 28, 2025

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About

Currently, I am pursuing a Ph.D. in Chemical Engineering at Virginia Tech, with an anticipated graduation in Spring 2025. My research focuses on integrating machine learning with quantum chemistry to accelerate catalyst design. I have developed interpretable machine learning models using PyTorch and SHAP to predict adsorption energies and d-band moments, allowing for both accurate material property predictions and deeper insights into catalytic mechanisms.

My work has led to successful collaborations, including the design of Ir-free trimetallic electrocatalysts for the ammonia oxidation reaction, which were experimentally validated and published in Nature Communications.

Beyond my doctoral research, I have hands-on experience in computational chemistry (DFT, Quantum Espresso, VASP) and molecular simulation techniques (MD, KMC), as well as expertise in large-scale simulations on high-performance computing clusters. Additionally, my prior experience as a research assistant at National Taiwan University involved developing refinery steam network simulation software for Formosa Petrochemical Corporation (FPC), demonstrating my ability to apply computational modeling to solve industrial-scale engineering problems.

Experience

Machine learning & Catalysis track:

Interpretable machine learning model (PyTorch, SHAP, Supervised/unsupervised learning, Physics-informed neural networks, Physics models development)
Quantum chemistry (DFT, Quantum Espresso, VASP)
Molecular simulation (Molecular dynamics, Monte Carlo, KMC, ab initio MD)
Catalysis design (Adsorption energy prediction, d-band moments modeling)
High-performance computing (Cluster computing, Large-scale data processing)

Modeling & simulation track: