Our Team

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.

Shengze received his BS in Physics from Stony Brook University and his BS in Applied Physics from Anhui University. He is an MS student working on computational modeling of materials synthesis. His work combines density functional theory, molecular dynamics, and machine learning to understand and predict the phase transformations that occur during the synthesis process.

Patrick is working on his BS in Materials Science and Engineering at UCLA. His work involves the use of machine learning interatomic potentials to model interfacial reactions between materials relevant to energy storage.

Ethan is pursuing his BS in Computer Engineering at UCLA. His research focuses on developing user-friendly web tools and interfaces for automated classification and visualization of materials characterization data.