https://zhantaochen.github.io/gamma-ut-austin/

Zhantao Chen is an Assistant Professor in the Walker Department of Mechanical Engineering at The University of Texas at Austin, where he leads the Group for AI in Materials Modeling and Analytics (GAMMA). Prior to joining UT Austin in 2025, Dr. Chen was a Research Associate at SLAC National Accelerator Laboratory (2022–2025). He received his Ph.D. in Mechanical Engineering from the Massachusetts Institute of Technology in 2022. Dr. Chen’s research develops artificial intelligence and machine learning methods for materials modeling, characterization, and discovery, with the vision of enabling AI-driven autonomous research platforms. His long-term goal is to integrate design, synthesis, and characterization into closed-loop, intelligent workflows that accelerate materials discovery and advance fundamental materials science.

Selected Publications

1. Chen, Z., Wang, C., Gao, M., Yoon, C. H., Thayer, J. B., & Turner, J. J. (2025). Augmenting X-ray single-particle imaging reconstruction with self-supervised machine learning. Newton, 1(4). https://doi.org/10.1016/j.newton.2025.100110 
2. Chen, Z., Petsch, A. N., Ji, Z., Chitturi, S. R., Peng, C., Jia, C., ... & Turner, J. J. (2025). Implicit neural representations for experimental steering of advanced experiments. Cell Reports Physical Science, 6(1). https://doi.org/10.1016/j.xcrp.2024.102333 
3. Liu, F., Chen, Z., Liu, T., Song, R., Lin, Y., Turner, J. J., & Jia, C. (2024). Self-supervised generative models for crystal structures. iScience, 27(9). https://doi.org/10.1016/j.isci.2024.110672 
4. Chen, Z., Peng, C., Petsch, A. N., Chitturi, S. R., Okullo, A., Chowdhury, S., ... & Turner, J. J. (2023). Bayesian experimental design and parameter estimation for ultrafast spin dynamics. Machine Learning: Science and Technology, 4(4), 045056. https://doi.org/10.1088/2632-2153/ad113a 
5. Chen, Z., Shen, X., Andrejevic, N., Liu, T., Luo, D., Nguyen, T., ... & Li, M. (2023). Panoramic mapping of phonon transport from ultrafast electron diffraction and scientific machine learning. Advanced Materials, 35(2), 2206997. https://doi.org/10.1002/adma.202206997 
6. Andrejevic, N., Chen, Z., Nguyen, T., Fan, L., Heiberger, H., Zhou, L. J., ... & Li, M. (2022). Elucidating proximity magnetism through polarized neutron reflectometry and machine learning. Applied Physics Reviews, 9(1). https://doi.org/10.1063/5.0078814 
7. Chen, Z., Andrejevic, N., Smidt, T., Ding, Z., Xu, Q., Chi, Y. T., ... & Li, M. (2021). Direct prediction of phonon density of states with Euclidean neural networks. Advanced Science, 8(12), 2004214. https://doi.org/10.1002/advs.202004214