Fu, Xiang

25 publications

ICML 2025 Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching Aaron J Havens, Benjamin Kurt Miller, Bing Yan, Carles Domingo-Enrich, Anuroop Sriram, Daniel S. Levine, Brandon M Wood, Bin Hu, Brandon Amos, Brian Karrer, Xiang Fu, Guan-Horng Liu, Ricky T. Q. Chen

ICLRW 2025 Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching Aaron J Havens, Benjamin Kurt Miller, Bing Yan, Carles Domingo-Enrich, Anuroop Sriram, Daniel S. Levine, Brandon M Wood, Bin Hu, Brandon Amos, Brian Karrer, Xiang Fu, Guan-Horng Liu, Ricky T. Q. Chen

ICML 2025 All-Atom Diffusion Transformers: Unified Generative Modelling of Molecules and Materials Chaitanya K. Joshi, Xiang Fu, Yi-Lun Liao, Vahe Gharakhanyan, Benjamin Kurt Miller, Anuroop Sriram, Zachary Ward Ulissi

ICLRW 2025 All-Atom Diffusion Transformers: Unified Generative Modelling of Molecules and Materials Chaitanya K. Joshi, Xiang Fu, Yi-Lun Liao, Vahe Gharakhanyan, Benjamin Kurt Miller, Anuroop Sriram, Zachary Ward Ulissi

FnTML 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

ICML 2025 Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction Xiang Fu, Brandon M Wood, Luis Barroso-Luque, Daniel S. Levine, Meng Gao, Misko Dzamba, C. Lawrence Zitnick

NeurIPS 2025 UMA: A Family of Universal Models for Atoms Brandon M Wood, Misko Dzamba, Xiang Fu, Meng Gao, Muhammed Shuaibi, Luis Barroso-Luque, Kareem Abdelmaqsoud, Vahe Gharakhanyan, John R. Kitchin, Daniel S. Levine, Kyle Michel, Anuroop Sriram, Taco Cohen, Abhishek Das, Sushree Jagriti Sahoo, Ammar Rizvi, Zachary Ward Ulissi, C. Lawrence Zitnick

NeurIPS 2024 A Recipe for Charge Density Prediction Xiang Fu, Andrew Rosen, Kyle Bystrom, Rui Wang, Albert Musaelian, Boris Kozinsky, Tess Smidt, Tommi Jaakkola

ICMLW 2024 A Recipe for Charge Density Prediction Xiang Fu, Andrew Scott Rosen, Kyle Bystrom, Rui Wang, Albert Musaelian, Boris Kozinsky, Tess Smidt, Tommi Jaakkola

ICLR 2024 MOFDiff: Coarse-Grained Diffusion for Metal-Organic Framework Design Xiang Fu, Tian Xie, Andrew Scott Rosen, Tommi S. Jaakkola, Jake Allen Smith

TMLR 2023 Forces Are Not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gomez-Bombarelli, Tommi Jaakkola

NeurIPSW 2023 Latent Space Simulator for Unveiling Molecular Free Energy Landscapes and Predicting Transition Dynamics Simon Dobers, Hannes Stark, Xiang Fu, Dominique Beaini, Stephan Günnemann

NeurIPSW 2023 Learning Interatomic Potentials at Multiple Scales Xiang Fu, Albert Musaelian, Anders Johansson, Tommi Jaakkola, Boris Kozinsky

NeurIPSW 2023 Learning Interatomic Potentials at Multiple Scales Xiang Fu, Albert Musaelian, Anders Johansson, Tommi Jaakkola, Boris Kozinsky

CoRL 2023 Learning to See Physical Properties with Active Sensing Motor Policies Gabriel B. Margolis, Xiang Fu, Yandong Ji, Pulkit Agrawal

NeurIPSW 2023 MOFDiff: Coarse-Grained Diffusion for Metal-Organic Framework Design Xiang Fu, Tian Xie, Andrew Scott Rosen, Tommi Jaakkola, Jake Allen Smith

NeurIPSW 2023 MOFDiff: Coarse-Grained Diffusion for Metal-Organic Framework Design Xiang Fu, Tian Xie, Andrew Scott Rosen, Tommi Jaakkola, Jake Allen Smith

TMLR 2023 Simulate Time-Integrated Coarse-Grained Molecular Dynamics with Multi-Scale Graph Networks Xiang Fu, Tian Xie, Nathan J. Rebello, Bradley Olsen, Tommi S. Jaakkola

ICLR 2022 Crystal Diffusion Variational Autoencoder for Periodic Material Generation Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, Tommi S. Jaakkola

NeurIPSW 2022 Forces Are Not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gomez-Bombarelli, Tommi S. Jaakkola

ICLRW 2022 Simulate Time-Integrated Coarse-Grained Molecular Dynamics with Geometric Machine Learning Xiang Fu, Tian Xie, Nathan J. Rebello, Bradley Olsen, Tommi S. Jaakkola

NeurIPSW 2021 Fragment-Based Sequential Translation for Molecular Optimization Benson Chen, Xiang Fu, Regina Barzilay, Tommi S. Jaakkola

ICML 2021 Learning Task Informed Abstractions Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi Jaakkola

CoRL 2021 Learning to Jump from Pixels Gabriel B Margolis, Tao Chen, Kartik Paigwar, Xiang Fu, Donghyun Kim, Sang bae Kim, Pulkit Agrawal

ICCV 2015 Robust Image Segmentation Using Contour-Guided Color Palettes Xiang Fu, Chien-Yi Wang, Chen Chen, Changhu Wang, C.-C. Jay Kuo