Cheng Zeng
Computational Materials Scientist at University of Florida.
Postdoctoral Associate
Email:c.zeng@ufl.edu or cheng_zeng1@alumni.brown.edu
Hi! I am currently a postdoc working with Prof. Mingjie Liu at University of Florida. My research focuses on molecular-graph generative models and AI-driven computational study on materials for energy applications. My research interests span a multitude of topics including materials design, machine learning, electrochemistry, generative modeling, 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 on highly interdisciplinary topics across computational materials science, additive manufacturing, wind energy and human-data interaciton, mentored by Prof. Nathan Post and 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. Before 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.
Recent news [archive]
| 2025/05 | Our PropMolFlow framework—now available online—uses flow matching parameterized by an SE(3)-equivariant graph neural network for conditional molecular generation. We demonstrate competitive performance against state-of-the-art diffusion models across a range of molecular properties, while maintaining high structural validity, stability, and sampling efficiency. To support reproducibility and further research, we also release two datasets: (1) DFT-computed properties for molecules generated with PropMolFlow, and (2) an updated QM9 SDF file (based on the DeepChem release) in which all bond-order and charge inconsistencies have been corrected. |
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| 2025/01 | I officially joined University of Florida Department of Chemistry as a postdoc to work on molecular-graph generative modeling and AI-driven computational simulations. |
| 2024/12 | Our work “Data-efficient and interpretable inverse materials design using a disentangled variational autoencoder” is published on line at AI & Materials. We employed disentangled variational autoencoder to develop a data-efficient and interpretable inverse materials design approach, which also has the potential of design materials with multiple target properties. |
