Chemical Engineer
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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.
Machine learning & Catalysis track:
PhD, Chemical Engineering, Virginia Tech, USA (GPA: 3.61/4.00) Sep. 2017 - Present
Master of Science, Chemical Engineering (GPA: 3.84/4.30, Ranking: 40/85) Sep. 2013 - Jun. 2015
National Taiwan University, Taiwan
Bachelor of Science, Chemical Engineering (GPA: 88.07/100.00, Ranking: 1/76) Sep. 2009 - Jun. 2013
National Taiwan University of Science and Technology, Taiwan