xxMD: Benchmarking Neural Force Fields Using Extended Dynamics beyond Equilibrium. - Zihan Pengmei; Yinan Shu; Junyu Liu (CoRR, 2023)

Transformers are efficient hierarchical chemical graph learners. - Zihan Pengmei; Zimu Li; Chih-chan Tien; Risi Kondor; Aaron R. Dinner (CoRR, 2023)

Technical Report: The Graph Spectral Token - Enhancing Graph Transformers with Spectral Information. - Zihan Pengmei; Zimu Li (CoRR, 2024)

geom2vec: pretrained GNNs as geometric featurizers for conformational dynamics. - Zihan Pengmei; Chatipat Lorpaiboon; Spencer C. Guo; Jonathan Weare; Aaron R. Dinner (CoRR, 2024)

Pushing the Limits of All-Atom Geometric Graph Neural Networks: Pre-Training, Scaling, and Zero-Shot Transfer. - Zihan Pengmei; Zhengyuan Shen; Zichen Wang 002; Marcus D. Collins; Huzefa Rangwala (ICLR, 2025)