Dr. Peichen Zhong is an Assistant Professor at the Department of Materials Science and Engineering, National University of Singapore. He obtained a BS in Physics from University of Science and Technology of China (USTC) in 2018, followed by a PhD in Materials Science from UC Berkeley in 2023 (Advisor: Prof. Gerbrand Ceder). He then completed the postdoctoral work at Lawrence Berkeley National Laboratory (LBNL) and Bakar Institute of Digital Materials for the Planet (BIDMaP), co-advised by Profs. Kristin A. Persson, Bingqing Cheng and Aditi Krishnapriyan. Previously, he was awarded the BIDMaP Emerging Scholar Fellowship from the College of Data Science, Computing and Society (CDSS) at UC Berkeley and the 2023 Rising Stars in Materials Science and Engineering by CMU/MIT/Stanford.
Research Interests
Dr. Zhong leads the Applied Machine Learning and Materials Modeling Group (The AM3 Group) at NUS MSE. We focus on both methodology development (including atomistic simulations with ML interatomic potentials and generative models) and their application to pioneer clean energy & sustainability technologies. Particularly, we are interested in
- Computational modeling of complex materials for renewable energy applications (e.g., Li/Na-ion battery cathodes/electrolytes/interfaces)
- Atomistic simulations with statistical mechanics & first-principles calculations in disordered/amorphous/interfacial systems
- AI for Science: machine learning interatomic potentials and generative models in scientific applications (e.g., chemical/solid-state reactions)
Selected Publications
- P. Zhong†, F. Xie†, L. Barroso-Luque, L. Huang, and G. Ceder*, “Modeling intercalation chemistry with multi‑redox reactions by sparse lattice models in disordered rocksalt cathodes”, PRX Energy 2, 043005 (2023).
- P. Zhong, S. Gupta, B. Deng, K. Jun, and G. Ceder*, “Effect of Cation Disorder on Lithium Transport in Halide Superionic Conductors”, ACS Energy Lett. 9, 2775 (2024).
- P. Zhong*, B. Deng, T. He, Z. Lun, and G. Ceder*, “Deep learning of experimental electrochemistry for battery cathodes across diverse compositions”, Joule 8, 1837–1854 (2024).
- P. Zhong†, D. Kim†, D. King†, B. Cheng*, “Machine learning interatomic potential can infer electrical response”, arXiv:2504.05169.
- P. Zhong*, X. Dai, B. Deng, G. Ceder, K. Persson*, “Practical approaches for crystal structure predictions with inpainting generation and foundation potentials”, arXiv:2504.16893.
- P. Zhong†, Z. Cai†, Y. Zhang, R. Giovine, B. Ouyang, G. Zeng, Y. Chen, R. Clément, Z. Lun, and G. Ceder, “Increasing Capacity in Disordered Rocksalt Cathodes by Mg Doping”, Chemistry of Materials 32, 10728 (2020).
- P. Zhong, T. Chen, L. Barroso‑Luque, F. Xie, G. Ceder*, “An ℓ0ℓ2‑norm regularized regression model for construction of robust cluster expansion in multicomponent systems ”, Phys. Rev. B 106, 024203 (2022).
- B. Deng, P. Zhong*, K. Jun, J. Riebesell, K. Han, C. J. Bartel, and G. Ceder*, “CHGNet as a pretrained universal neural network potential for charge‑informed atomistic modeling”, Nature Machine Intelligence 5, 1031–1041 (2023).
- L. Huang†, P. Zhong†, Y. Ha, Z. Cai, Y.‑W. Byeon, T.‑Y. Huang, Y. Sun, F. Xie, H.‑M. Hau, H. Kim, M. Balasubramanian, B. D. McCloskey, W. Yang, and G. Ceder*, “Optimizing Li‑Excess Cation‑Disordered Rocksalt Cathode Design Through Partial Li Deficiency”, Advanced Energy Materials 13, 2202345 (2023).
- D. Kim†, D. King†, P. Zhong†, B. Cheng*, “Learning charges and long‑range interactions from energies and forces”, arXiv:2412.15455 (2024).