Cheng Zeng

Hi! I am currently a postdoct working with Prof. Mingjie Liu at University of Florida. My research focuses on molecular-graph diffusion models and AI-driven computational study on materials for energy applications.My research interests spans a multitude of topics including materials design, machine learning, electrochemistry, computational catalysis and renewable energy. I am interested in solving complex engineering problems with machine learning, physical simulations and numerical methods.

From 2022 to 2024, I worked at the Instutite for Experiential AI and Roux institute of Northeastern University, mentored by Prof. Nathan Post and Prof. Jack Lesko. Prior to joining Roux, I received a PhD in Engineering at Brown University. En route to my PhD degree, I also received a secondary Master’s degree in Data Science via Brown’s Open Graduate Education Program. At Brown, I worked at the Catalyst Design Lab under the direction of Andrew Peterson. My PhD research focused on machine learning potentials and force–displacement phenomenological models for rational catalyst design. A key highlight of my work is that a combination of simulation techniques—including machine learning interatomic potential trained using a nearsighted force-training approach, a phenomenological model termed eigenforce model, a geometric descriptor termed generalized coordination number and a microkinetic analysis based on DFT-calculated free energy reaction pathway–generates trends of surface catalytic activity of nanoparticle electrocatalysts in various sizes and alloy compositions. Prior to Brown, I got a Bachelor and Master in Materials Science and Engineering from Tsinghua University. At Tsinghua, I worked with Yunhan Ling on oxide films/coatings resistant to hydrogen permeation.