Cong Fu

According to our database, Cong Fu authored at least 13 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

Tensor Decomposition Networks for Fast Machine Learning Interatomic Potential Computations.

Yuchao Lin, Cong Fu, Zachary Krueger, Haiyang Yu, Maho Nakata, Jianwen Xie, Emine Küçükbenli, Xiaofeng Qian, Shuiwang Ji

CoRR, July, 2025

Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials.

Cong Fu, Yuchao Lin, Zachary Krueger, Haiyang Yu, Maho Nakata, Jianwen Xie, Emine Küçükbenli, Xiaofeng Qian, Shuiwang Ji

CoRR, July, 2025

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems.

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, Alex Strasser, Haiyang Yu, Yuqing Xie, Xiang Fu, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, 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, Alán Aspuru-Guzik, Erik J. Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji

Found. Trends Mach. Learn., 2025

Fragment and Geometry Aware Tokenization of Molecules for Structure-Based Drug Design Using Language Models.

Cong Fu, Xiner Li, Blake Olson, Heng Ji, Shuiwang Ji

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

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, Akosua Busia, Abhishaike Mahajan, Stephen Malina, Neha Prasad, Sam Sinai, Lindsay Edwards, Thomas Gaudelet, Cristian Regep, Martin Steinegger, Burkhard Rost, Alexander Brace, Kyle Hippe, Luca Naef, Keisuke Kamata, George Armstrong, Kevin Boyd, Zhonglin Cao, Han-Yi Chou, Simon Chu, Allan dos Santos Costa, Sajad Darabi, Eric Dawson, Kieran Didi, Cong Fu, Mario Geiger, Michelle Gill, Darren Hsu, Gagan Kaushik, Maria Korshunova, Steven Kothen-Hill, Youhan Lee, Meng Liu, Micha Livne, Zachary McClure, Jonathan Mitchell, Alireza Moradzadeh, Ohad Mosafi, Youssef Nashed, Saee Paliwal, Yuxing Peng, Sara Rabhi, Farhad Ramezanghorbani, Danny Reidenbach, Camir Ricketts, Brian Roland, Kushal Shah, Tyler Shimko, Hassan Sirelkhatim, Savitha Srinivasan, Abraham C. Stern, Dorota Toczydlowska, Srimukh Prasad Veccham, Niccolò Alberto Elia Venanzi, Anton Vorontsov, Jared Wilber, Isabel Wilkinson, Wei Jing Wong, Eva Xue, Cory Ye, Xin Yu, Yang Zhang, Guoqing Zhou, Becca Zandstein, Christian Dallago, Bruno Trentini, Emine Küçükbenli, Timur Rvachov, Eddie Calleja, Johnny Israeli, Harry Clifford, Risto Haukioja, Nicholas Haemel, Kyle Tretina, Neha Tadimeti, Anthony B. Costa

CoRR, 2024

Lattice Convolutional Networks for Learning Ground States of Quantum Many-Body Systems.

Cong Fu, Xuan Zhang, Huixin Zhang, Hongyi Ling, Shenglong Xu, Shuiwang Ji

Proceedings of the 2024 SIAM International Conference on Data Mining, 2024

SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations.

Xuan Zhang, Jacob Helwig, Yuchao Lin, Yaochen Xie, Cong Fu, Stephan Wojtowytsch, Shuiwang Ji

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Complete and Efficient Graph Transformers for Crystal Material Property Prediction.

Keqiang Yan, Cong Fu, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

A Latent Diffusion Model for Protein Structure Generation.

Cong Fu, Keqiang Yan, Limei Wang, Wing Yee Au, Michael McThrow, Tao Komikado, Koji Maruhashi, Kanji Uchino, Xiaoning Qian, Shuiwang Ji

Proceedings of the Learning on Graphs Conference, 27-30 November 2023, Virtual Event., 2023

Semi-Supervised Learning for High-Fidelity Fluid Flow Reconstruction.

Cong Fu, Jacob Helwig, Shuiwang Ji

Proceedings of the Learning on Graphs Conference, 27-30 November 2023, Virtual Event., 2023

Group Equivariant Fourier Neural Operators for Partial Differential Equations.

Jacob Helwig, Xuan Zhang, Cong Fu, Jerry Kurtin, Stephan Wojtowytsch, Shuiwang Ji

Proceedings of the International Conference on Machine Learning, 2023

2021

DIG: A Turnkey Library for Diving into Graph Deep Learning Research.

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, Shuiwang Ji

J. Mach. Learn. Res., 2021

Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks.

Meng Liu, Cong Fu, Xuan Zhang, Limei Wang, Yaochen Xie, Hao Yuan, Youzhi Luo, Zhao Xu, Shenglong Xu, Shuiwang Ji

CoRR, 2021