Selected Papers

MatterGen: 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]