Xiang Fu

According to our database, Xiang Fu authored at least 18 papers between 2019 and 2024.

Collaborative distances:

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

2024

Virtual node graph neural network for full phonon prediction.

Ryotaro Okabe, Abhijatmedhi Chotrattanapituk, Artittaya Boonkird, Nina Andrejevic, Xiang Fu, Tommi S. Jaakkola, Qichen Song, Thanh Nguyen, Nathan C. Drucker, Sai Mu, Yao Wang, Bolin Liao, Yongqiang Cheng, Mingda Li

Nat. Comput. Sci., July, 2024

Learning to Model Atoms Across Scales

Xiang Fu

PhD thesis, 2024

Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates.

Ryotaro Okabe, Mouyang Cheng, Abhijatmedhi Chotrattanapituk, Nguyen Tuan Hung, Xiang Fu, Bowen Han, Yao Wang, Weiwei Xie, Robert J. Cava, Tommi S. Jaakkola, Yongqiang Cheng, Mingda Li

CoRR, 2024

A Recipe for Charge Density Prediction.

Xiang Fu, Andrew S. Rosen, Kyle Bystrom, Rui Wang, Albert Musaelian, Boris Kozinsky, Tess E. Smidt, Tommi S. Jaakkola

CoRR, 2024

MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design.

Xiang Fu, Tian Xie, Andrew S. Rosen, Tommi S. Jaakkola, Jake Smith

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-scale Graph Networks.

Xiang Fu, Tian Xie, Nathan J. Rebello, Bradley D. Olsen, Tommi S. Jaakkola

Trans. Mach. Learn. Res., 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 Gómez-Bombarelli, Tommi S. Jaakkola

Trans. Mach. Learn. Res., 2023

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, Jake Smith, Ryota Tomioka, Tian Xie

CoRR, 2023

Learning Interatomic Potentials at Multiple Scales.

Xiang Fu, Albert Musaelian, Anders Johansson, Tommi S. Jaakkola, Boris Kozinsky

CoRR, 2023

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, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, 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

CoRR, 2023

Learning to See Physical Properties with Active Sensing Motor Policies.

Gabriel B. Margolis, Xiang Fu, Yandong Ji, Pulkit Agrawal

Proceedings of the Conference on Robot Learning, 2023

2022

Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning.

Xiang Fu, Tian Xie, Nathan J. Rebello, Bradley D. Olsen, Tommi S. Jaakkola

CoRR, 2022

Crystal Diffusion Variational Autoencoder for Periodic Material Generation.

Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, Tommi S. Jaakkola

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Fragment-based Sequential Translation for Molecular Optimization.

Benson Chen, Xiang Fu, Regina Barzilay, Tommi S. Jaakkola

CoRR, 2021

Modelling and analysis of tagging networks in Stack Exchange communities.

Xiang Fu, Shangdi Yu, Austin R. Benson

J. Complex Networks, 2021

Learning Task Informed Abstractions.

Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi S. Jaakkola

Proceedings of the 38th International Conference on Machine Learning, 2021

Learning to Jump from Pixels.

Gabriel B. Margolis, Tao Chen, Kartik Paigwar, Xiang Fu, Donghyun Kim, Sangbae Kim, Pulkit Agrawal

Proceedings of the Conference on Robot Learning, 8-11 November 2021, London, UK., 2021

2019

Modeling and Analysis of Tagging Networks in Stack Exchange Communities.

Xiang Fu, Shangdi Yu, Austin R. Benson

CoRR, 2019