Selected Papers

Thermodynamically Informed Multimodal Learning of High-Dimensional Free Energy Models in Molecular Coarse Graining.
Blake Duschatko, Xiang Fu, Cameron Owen, Yu Xie, Albert Musaelian, Tommi Jaakkola, Boris Kozinsky.
Preprint, 2024.
[paper]

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]

Virtual Node Graph Neural Network for Full Phonon Prediction.
Ryotaro Okabe, Abhijatmedhi Chotrattanapituk, Artittaya Boonkird, Nina Andrejevic, Xiang Fu, Tommi Jaakkola, Qichen Song, Thanh Nguyen, Nathan Drucker, Sai Mu, Bolin Liao, Yongqiang Cheng, Mingda Li
Nature Computational Science, 2024.
[paper]

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]

MatterGen: a generative model for inorganic materials design.
Claudio Zeni*, Robert Pinsler*, Daniel Zügner*, Andrew Fowler*, Matthew Horton*, Xiang Fu, Sasha Shysheya, Jonathan Crabbé, Lixin Sun, Bichlien Nguyen, Hannes Schulz, Sarah Lewis, Chin-Wei Huang, Ziheng Lu, Yichi Zhou, Han Yang, Hongxia Hao, Jielan Li, Ryota Tomioka*, Tian Xie*.
Preprint, 2023.
[paper]

Learning to See Physical Properties with Active Sensing Motor Policies.
Gabriel Margolis, Xiang Fu, Yandong Ji, Pulkit Agrawal
Conference on Robot Learning (CoRL), 2023.
[website] [paper]

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.
[website] [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 to Jump from Pixels.
Gabriel Margolis, Tao Chen, Kartik Paigwar, Xiang Fu, Donghyun Kim, Sangbae Kim, Pulkit Agrawal.
Conference on Robot Learning (CoRL), 2021.
[website] [paper]

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