Rate My Professor Wen Wen

Mingjian Wen is an assistant professor in the Department of Chemical and Biomolecular Engineering at the University of Houston's Cullen College of Engineering. He joined the faculty in September 2022 as a Presidential Frontier Faculty Fellow. Wen holds a Ph.D. in Aerospace Engineering and Mechanics from the University of Minnesota-Twin Cities (2019), where his dissertation focused on the development of interatomic potentials with uncertainty quantification applied to two-dimensional materials, under the supervision of Prof. Ellad B. Tadmor. He earned his B.S. in Chemical Engineering from Tianjin University in 2012. Prior to his current role, he was a postdoctoral researcher in the Energy Storage and Distributed Resources Division at Lawrence Berkeley National Laboratory (2019-2022), mentored by Prof. Kristin A. Persson, contributing to the Materials Project and advancing machine learning models for battery materials.

Wen's research centers on data-driven materials modeling, leveraging machine learning, high-performance computing, and atomistic simulations to design novel materials for energy storage, catalysis, and healthcare applications. His key contributions include developing equivariant graph neural networks for predicting material properties such as elasticity tensors and NMR shifts, and frameworks like KLIFF for interatomic potentials. Notable publications are "Chemical reaction networks and opportunities for machine learning" in Nature Computational Science (2023), "Machine learning full NMR tensors with equivariant graph neural networks" in the Journal of Physical Chemistry A (2023), and "KLIFF: A framework to develop physics-based and machine learning interatomic potential" in Computer Physics Communications (2021). He has received the NSF LEAPS-MPS grant ($250,000, 2023-2025) as PI and awards including the UH Alternative Textbook Incentive Award (2023) and Doctoral Dissertation Fellowship (2018). Wen maintains active service as symposium chair for AIChE sessions and reviewer for prestigious journals. He developed open-source tools like MatTen and BonDNet, impacting computational materials science.