Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks. - Meng Liu 015; Cong Fu 003; Xuan Zhang; Limei Wang; Yaochen Xie; Hao Yuan 001; Youzhi Luo; Zhao Xu 005; Shenglong Xu; Shuiwang Ji (CoRR, 2021)

DIG: A Turnkey Library for Diving into Graph Deep Learning Research. - Meng Liu 015; Youzhi Luo; Limei Wang; Yaochen Xie; Hao Yuan 001; Shurui Gui; Haiyang Yu 005; Zhao Xu 005; Jingtun Zhang; Yi Liu 059; Keqiang Yan; Haoran Liu; et al. (J. Mach. Learn. Res., 2021)

Group Equivariant Fourier Neural Operators for Partial Differential Equations. - Jacob Helwig; Xuan Zhang; Cong Fu 003; Jerry Kurtin; Stephan Wojtowytsch; Shuiwang Ji (ICML, 2023)

Semi-Supervised Learning for High-Fidelity Fluid Flow Reconstruction. - Cong Fu 003; Jacob Helwig; Shuiwang Ji (LoG, 2023)

A Latent Diffusion Model for Protein Structure Generation. - Cong Fu 003; Keqiang Yan; Limei Wang; Wing Yee Au 002; Michael McThrow; Tao Komikado; Koji Maruhashi; Kanji Uchino; Xiaoning Qian; Shuiwang Ji (LoG, 2023)

Complete and Efficient Graph Transformers for Crystal Material Property Prediction. - Keqiang Yan; Cong Fu 003; Xiaofeng Qian; Xiaoning Qian; Shuiwang Ji (ICLR, 2024)

SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations. - Xuan Zhang; Jacob Helwig; Yuchao Lin; Yaochen Xie; Cong Fu 003; Stephan Wojtowytsch; Shuiwang Ji (ICLR, 2024)

Lattice Convolutional Networks for Learning Ground States of Quantum Many-Body Systems. - Cong Fu 003; Xuan Zhang; Huixin Zhang; Hongyi Ling; Shenglong Xu; Shuiwang Ji (SDM, 2024)

BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery. - Peter St. John; Dejun Lin; Polina Binder; Malcolm Greaves; Vega Shah; John St. John; Adrian Lange; Patrick D. Hsu; Rajesh Illango; Arvind Ramanathan; Anima Anandkumar; David H. Brookes; et al. (CoRR, 2024)

Fragment and Geometry Aware Tokenization of Molecules for Structure-Based Drug Design Using Language Models. - Cong Fu 003; Xiner Li; Blake Olson; Heng Ji 001; Shuiwang Ji (ICLR, 2025)

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. - Xuan Zhang; Limei Wang; Jacob Helwig; Youzhi Luo; Cong Fu 003; Yaochen Xie; Meng Liu 015; Yuchao Lin; Zhao Xu 005; Keqiang Yan; Keir Adams; Maurice Weiler; et al. (Found. Trends Mach. Learn., 2025)

Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials. - Cong Fu 003; Yuchao Lin; Zachary Krueger; Haiyang Yu 005; Maho Nakata; Jianwen Xie; Emine Küçükbenli; Xiaofeng Qian; Shuiwang Ji (CoRR, 2025)

Tensor Decomposition Networks for Fast Machine Learning Interatomic Potential Computations. - Yuchao Lin; Cong Fu 003; Zachary Krueger; Haiyang Yu 005; Maho Nakata; Jianwen Xie; Emine Küçükbenli; Xiaofeng Qian; Shuiwang Ji (CoRR, 2025)