Summary
Peichen Zhong is an Assistant Professor of Materials Science and Engineering (NUS Presidential Young Professorship, starting July 2025) and a computational materials scientist with eight years’ experience bridging first-principles modeling, statistical mechanics, and AI-driven atomistic simulation. Trained at UC Berkeley (PhD 2023) and USTC, he has developed and applied DFT, molecular dynamics, cluster expansion, ML interatomic potentials, and generative models to complex energy materials such as disordered rocksalt cathodes for Li-ion batteries. His postdoc and research fellow roles at Berkeley Lab and UC Berkeley combined rigorous methodological development with practical materials design, often integrating mathematical programming and deep learning. Comfortable moving between theory, algorithm development, and scalable computation, he brings a rare blend of condensed-matter physics intuition and modern ML toolkits to accelerate sustainable materials discovery. Notably, his work emphasizes modeling disorder and transport in solids—areas where conventional methods struggle—positioning him to push AI-for-science approaches in materials engineering.
8 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD Materials Science, Doctor of Philosophy - PhD Materials Science at University of California, Berkeley
Chengdu NO.7 High School 成都市第七中学
Bachelor of Science - BS Physics, Bachelor of Science - BS Physics at University of Science and Technology of China