Mingjian Wen

Mingjian Wen is an Adjunct Assistant Professor in the William A. Brookshire Department of Chemical and Biomolecular Engineering at the University of Houston, affiliated with the Cullen College of Engineering. He holds the Presidential Frontier Faculty Fellow title. His research focuses on developing data-driven and machine learning methods, molecular simulations, and open cyberinfrastructure to design materials and chemical reactions for renewable energy. He completed a postdoc at Lawrence Berkeley National Lab, a Ph.D. in Aerospace Engineering & Mechanics from the University of Minnesota, and a B.S. in Chemical Engineering from Tianjin University.

Education:

• Ph.D., Aerospace Engineering and Mechanics, University of Minnesota-Twin Cities, 2019
• B.S., Chemical Engineering, Tianjin University, 2012
• Postdoc, Energy Technologies Areas, Lawrence Berkeley National Laboratory, 2022

Research interests emphasize integrating machine learning with computational chemistry to address challenges in renewable energy, materials design, and sustainable processes. His work spans uncertainty quantification in atomistic models, graph neural networks for molecular properties, and open-source tools like Atomate2 and Jobflow for materials informatics.

Scientific awards include the University of Minnesota Doctoral Dissertation Fellowship and the UH Alternative Textbook Incentive Award. His lab (wengroup.github.io) explores interdisciplinary approaches to accelerate materials discovery through AI-driven methods.

Dr. Wen’s contributions span computational workflows, hydrolysis energy predictions (HEPOM framework), and equivariant neural networks for crystal elasticity. His research bridges theory and application, aiming to advance sustainable energy technologies through advanced modeling and automation.