Nathan is a materials scientist working to advance sustainable materials by integrating computational and experimental methods. His research combines ab-initio quantum chemistry, machine learning, and kinetic Monte Carlo simulations to model solid-state inorganic materials synthesis. By pairing these techniques with in-situ characterization and AI-driven data analysis, his group streamlines the process between discovery and commercialization of novel energy materials.
Prior to joining UCLA, Nathan completed his PhD at UC Berkeley with Prof. Gerbrand Ceder and was a postdoc with Prof. Chris Bartel at the University of Minnesota. Nathan's work has been recognized with awards including the NSF Graduate Research Fellowship, Didier de Fontaine Award in Theory and Computation, and Jane Lewis Fellowship in Mining.
Xinyang received his BS in Materials Physics from the University of Science and Technology of China (USTC). He is a PhD student working on AI for X-ray diffraction. His work leverages deep learning to automate the collection and analysis of characterization data, providing open-source foundation models for the materials community.
We are always looking for motivated graduate and undergraduate students to join our group. Prospective graduate students should apply through the UCLA Materials Science and Engineering Department. Undergraduate students at UCLA interested in research opportunities should email Prof. Szymanski with a brief description of your interests and background.