smol: A Python package for cluster expansions and beyond. - Luis Barroso-Luque; Julia H. Yang; Fengyu Xie; Tina Chen; Ronald L. Kam; Zinab Jadidi; Peichen Zhong; Gerbrand Ceder (J. Open Source Softw., 2022)

CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling. - Bowen Deng; Peichen Zhong; KyuJung Jun; Kevin Han; Christopher J. Bartel; Gerbrand Ceder (CoRR, 2023)

Computational Modeling of Multicomponent Disordered Rocksalt Cathodes - Peichen Zhong (2023)

CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling. - Bowen Deng; Peichen Zhong; KyuJung Jun; Janosh Riebesell; Kevin Han; Christopher J. Bartel; Gerbrand Ceder (Nat. Mac. Intell., 2023)

Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning. - Bowen Deng; Yunyeong Choi; Peichen Zhong; Janosh Riebesell; Shashwat Anand; Zhuohan Li; KyuJung Jun; Kristin A. Persson; Gerbrand Ceder (CoRR, 2024)

Learning charges and long-range interactions from energies and forces. - Dongjin Kim; Daniel S. King; Peichen Zhong; Bingqing Cheng (CoRR, 2024)

Large Language Models Are Innate Crystal Structure Generators. - Jingru Gan; Peichen Zhong; Yuanqi Du; Yanqiao Zhu 001; Chenru Duan; Haorui Wang; Carla P. Gomes; Kristin A. Persson; Daniel Schwalbe-Koda; Wei Wang 010 (CoRR, 2025)

Machine learning interatomic potential can infer electrical response. - Peichen Zhong; Dongjin Kim; Daniel S. King; Bingqing Cheng (CoRR, 2025)

Cross-functional transferability in universal machine learning interatomic potentials. - Xu Huang; Bowen Deng; Peichen Zhong; Aaron D. Kaplan; Kristin A. Persson; Gerbrand Ceder (CoRR, 2025)

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials. - Dongjin Kim; Xiaoyu Wang; Peichen Zhong; Daniel S. King; Théo Jaffrelot Inizan; Bingqing Cheng (CoRR, 2025)