Selected Papers

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, Ammar Rizvi, Sushree Jagriti Sahoo, Zachary W. Ulissi, C. Lawrence Zitnick.
Preprint, 2025.
[paper] [code] [checkpoint]

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.
International Conference on Machine Learning (ICML), 2025.
[paper] [code] [checkpoint]

A Generative Model for Inorganic Materials Design.
Claudio Zeni*, Robert Pinsler*, Daniel Zügner*, Andrew Fowler*, Matthew Horton*, Xiang Fu, Zilong Wang, Aliaksandra Shysheya, Jonathan Crabbé, Shoko Ueda, Roberto Sordillo, Lixin Sun, Jake Smith, Bichlien Nguyen, Hannes Schulz, Sarah Lewis, Chin-Wei Huang, Ziheng Lu, Yichi Zhou, Han Yang, Hongxia Hao, Jielan Li, Chunlei Yang, Wenjie Li, Ryota Tomioka*, Tian Xie*.
Nature, 2025.
[paper] [code]

A Recipe for Charge Density Prediction.
Xiang Fu, Andrew Rosen, Kyle Bystrom, Rui Wang, Albert Musaelian, Boris Kozinsky, Tess Smidt, Tommi Jaakkola.
Neural Information Processing Systems (NeurIPS), 2024.
[paper] [code]

MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design.
Xiang Fu, Tian Xie, Andrew Rosen, Tommi Jaakkola, Jake Smith.
International Conference on Learning Representations (ICLR), 2024.
[paper] [code]

Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-scale Graph Networks.
Xiang Fu*, Tian Xie*, Nathan J. Rebello, Bradley D. Olsen, Tommi Jaakkola.
Transactions on Machine Learning Research (TMLR), 2023.
[paper] [code]

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.
Transactions on Machine Learning Research (TMLR), 2023.
[paper] [code]

Crystal Diffusion Variational Autoencoder for Periodic Material Generation.
Tian Xie*, Xiang Fu*, Octavian Ganea*, Regina Barzilay, Tommi Jaakkola.
International Conference on Learning Representations (ICLR), 2022.
[paper] [code]

Learning Task Informed Abstractions.
Xiang Fu*, Ge Yang*, Pulkit Agrawal, Tommi Jaakkola.
International Conference on Machine Learning (ICML), 2021.
[paper] [code]