Highlighted Preprints

* indicates equal contribution. † indicates equal senior contribution (co-corresponding authorship). ___ indicates the students from our lab at Stony Brook.

• Wenhan Gao*, Jian Luo*, Fang Wan, Ruichen Xu, Xiang Liu, Haipeng Xing, Yi Liu
  Can Data-Driven Dynamics Reveal Hidden Physics? There Is A Need for Interpretable Neural Operators
  arXiv: 2510.02683, 2025.
  


• Fang Wan, Wenhan Gao, Jingxiang Qu, Yi Liu
  Diffuse and Steer: Corrective Sampling for Stable 3D Molecular Generation
  Accepted to ICLR 2026 Workshop on Real-World Constrained and Preference-Aligned Flow and Diffusion-Based Models.
  


• Xufeng Liu*, Pranab Sarker*, Size Zheng†, Yi Liu†, Tao Wei†
  Machine-Learning Forcefield Molecular Dynamics Simulations of Hydration Behavior of TMAO Zwitterion
  arXiv, 2026.
  

Selected Publications

* indicates equal contribution. † indicates equal senior contribution (co-corresponding authorship). ___ indicates the students from our lab at Stony Brook.

• Jingxiang Qu, Wenhan Gao, Ruichen Xu, Yi Liu
  GAGA: Gaussianity-Aware Gaussian Approximation for Efficient 3D Molecular Generation
  International Conference on Learning Representations (ICLR), 2026
  Also presented at NeurIPS 2025 Workshop on Structured Probabilistic Inference & Generative Modeling.
  


• Xufeng Liu*, Yixuan Ding*, Jingxiang Qu, Yichi Zhang, Wenhan Gao†, Yi Liu†
  RL-Guider: Leveraging Historical Decisions and Feedback for Drug Editing with Large Language Models
  Findings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL Findings), 2025.
  


• Jingxiang Qu*, Wenhan Gao*, Jiaxing Zhang, Xufeng Liu, Hua Wei, Haibin Ling, Yi Liu
  RISE: Radius of Influence based Subgraph Extraction for 3D Molecular Graph Explanation
  The 42nd International Conference on Machine Learning (ICML), 2025.
  


• Wenhan Gao, Jian Luo, Ruichen Xu, Yi Liu
  Dynamic Schwartz-Fourier Neural Operator for Enhanced Expressive Power
  Transactions on Machine Learning Research (TMLR), 2025.
  


• Wenhan Gao, Ruichen Xu, Yuefan Deng, Yi Liu
  Discretization-invariance? On the Discretization Mismatch Errors in Neural Operators
  International Conference on Learning Representations (ICLR), 2025.
  


• Xufeng Liu, Dongsheng Luo, Wenhan Gao, Yi Liu
  3DGraphX: Explaining 3D Molecular Graph Models via Incorporating Chemical Priors
  The 31st SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2025.
  


• Ronast Subedi*, Lu Wei*, Wenhan Gao*, Shayok Chakraborty, Yi Liu
  Empowering Active Learning for 3D Molecular Graphs with Geometric Graph Isomorphism
  The 38th Annual Conference on Neural Information Processing Systems (NeurIPS), 2024.
  


• Wenhan Gao, Ruichen Xu, Hong Wang, Yi Liu
  Coordinate Transform Fourier Neural Operators for Symmetries in Physical Modelings
  Transactions on Machine Learning Research (TMLR), 2024.
  


• Xuan Zhang*, Limei Wang*, Jacob Helwig*, Youzhi Luo*, Cong Fu*, Yaochen Xie*, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, YuQing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stark, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alan Aspuru-Guzik, Erik Bekkers, Michael Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Lio, Rose Yu, Stephan Gunnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess Smidt, and Shuiwang Ji
  Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
  Foundations and Trends in Machine Learning, 2025.
  [Paper] [Website]


• Yuchao Lin, Keqiang Yan, Youzhi Luo, Yi Liu, Xiaoning Qian, and Shuiwang Ji
  Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction
  The 40th International Conference on Machine Learning (ICML), 2023.
  


• Limei Wang*, Yi Liu*, Yuchao Lin, Haoran Liu, and Shuiwang Ji
  ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs
  The 36th Annual Conference on Neural Information Processing Systems (NeurIPS), 2022.
  


• Keqiang Yan, Yi Liu†, Yuchao Lin, and Shuiwang Ji†
  Periodic Graph Transformers for Crystal Material Property Prediction
  The 36th Annual Conference on Neural Information Processing Systems (NeurIPS), 2022.
  


• Yi Liu*, Limei Wang*, Meng Liu, Yuchao Lin, Xuan Zhang, Bora Oztekin, and Shuiwang Ji
  Spherical Message Passing for 3D Molecular Graphs
  International Conference on Learning Representations (ICLR), 2022.
  


• Yi Liu and Shuiwang Ji
  CleftNet: Augmented Deep Learning for Synaptic Cleft Detection from Brain Electron Microscopy
  IEEE Transactions on Medical Imaging (TMI), 2021.
  


• Hongyang Gao*, Yi Liu*, and Shuiwang Ji
  Topology-Aware Graph Pooling Networks
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021.
  


• Meng Liu*, Youzhi Luo*, Limei Wang*, Yaochen Xie*, Hao Yuan*, Shurui Gui*, Haiyang Yu*, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora Oztekin, Xuan Zhang, and Shuiwang Ji
  DIG: A Turnkey Library for Diving into Graph Deep Learning Research
  Journal of Machine Learning Research (JMLR), 2021.
  


• Yi Liu, Hao Yuan, Lei Cai, and Shuiwang Ji
  Deep Learning of High-Order Interactions for Protein Interface Prediction
  The 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020.
  


• Yi Liu, Hao Yuan, Zhengyang Wang, and Shuiwang Ji
  Global Pixel Transformers for Virtual Staining of Microscopy Images
  IEEE Transactions on Medical Imaging (TMI), 2019.