Dr. Zongrui Pei holds a Ph.D. in computational materials science. Dr. Pei’s expertise includes computational materials science, high-performance computing, classical/quantum machine learning, and mathematical physics. He has over 10 years of experience in multiscale materials simulation and modeling and over 7 years of experience in artificial intelligence/machine learning. He developed many machine learning models for materials design using structured data, images, and text. These models include large language models, AI agents, quantum computing and quantum machine learning.

He is currently a faculty member in the Department of Physics and the Division of Data Science at the University of Texas at Arlington. Previously, he was a senior computational staff member at New York University. He is an editor of several journals, including Next Materials, Next Energy, and Discover Applied Science. He also wrote a book on modern computational materials science. His current research focuses include developing physics-based methods for materials design, AI-for-science models, quantum algorithm design, automated quantum circuit generation, and using high-performance computing to investigate materials.

His current research is supported by Rise100 initiative AI cluster hire through Provost's office, the startup from the College of Science, and the STAR program at the University of Texas at Arlington.

We are currently recruiting graduate students, postdocs, research scientists/professors.

Some awards:

Top 2% Scientist over the last five years (according to Stanford/Elsevier, 2021-2025), Stanford/Elsevier. (2025).

Best paper-fundamental research award, The Minerals, Metals & Materials Society (TMS). (2022).

Excellent referee award of Modell. Simul. Mater. Sci. Eng., IOP. (2018).

Excellent referee awards of Comput. Mater. Sci., Elsevier. (2017).

Max-Planck Scholarship, Max-Planck Association. (2015).