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.
YongGeon received his BS in Fine Chemical Engineering from Chungnam National University and his MS in Information Studies from the University of Texas at Austin. He is a PhD student working on the simulation of electrified interfaces. His work uses machine learning interatomic potentials to model how ions move and react under an applied electrochemical potential, with a focus on the stability of catalysts for hydrogen production.
Jiahan received her BS in Chemistry from University College London. She is an MS student working on the development of generative AI models to predict atomic-scale transitions for kinetic Monte Carlo simulations of materials dynamics.
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.
Maya received her BS in Electrical Engineering from UC San Diego. She is an MS student working on the synthesis of metastable materials. Her work uses in-situ X-ray diffraction to monitor and control the diffusion-limited phase transformations that occur during solid-state reactions, with the goal of trapping useful metastable phases.
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.
Elisabeth is pursuing her BS in Materials Engineering at UCLA. Her research focuses on collecting solid-state reaction data using in-situ high-temperature powder X-ray diffraction.
Aditya is pursuing his BS in Mechanical Engineering at UCLA. His research focuses on developing robotics and automation for high-throughput solid-state synthesis of inorganic materials.
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.
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.