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ORCIDXiang Fu 0005
Person information
- affiliation: Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA
- affiliation: Cornell University, Department of Computer Science, Ithaca, NY, USA
Other persons with the same name
- Xiang Fu — disambiguation page
- Xiang Fu 0001
![[0000-0002-6608-1654]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-6608-1654")
— Hofstra University, Department of Computer Science, Hempstead, NY, USA (and 2 more) - Xiang Fu 0002 — National University of Defense Technology, College of Computer, Key Laboratory of Software Engineering for Complex Systems, Changsha, China (and 2 more)
- Xiang Fu 0003 — National University of Defense Technology, College of Computer, Institute for Quantum Information & State Key Laboratory of High Performance Computing (HPCL), Changsha, China (and 1 more)
- Xiang Fu 0004 — Nanchang Hangkong University, School of Software, Nanchang, China (and 1 more)
- Xiang Fu 0006 — University of Southern California, Department of Electrical Engineering, Los Angeles, CA, USA
- Xiang Fu 0007 — Chinese Academy and Sciences, Institute of Computing Technology, Key Laboratory of Computer System and Architecture, Beijing, China
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2020 – today
- 2025
[i15]Chaitanya K. Joshi, Xiang Fu, Yi-Lun Liao, Vahe Gharakhanyan, Benjamin Kurt Miller, Anuroop Sriram, Zachary W. Ulissi:
All-atom Diffusion Transformers: Unified generative modelling of molecules and materials. CoRR abs/2503.03965 (2025)- 2024
- [b1]Xiang Fu:
Learning to Model Atoms Across Scales. MIT, USA, 2024 - [j4]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:
Virtual node graph neural network for full phonon prediction. Nat. Comput. Sci. 4(7): 522-531 (2024) - [c6]Xiang Fu, Tian Xie, Andrew S. Rosen, Tommi S. Jaakkola, Jake Smith:
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design. ICLR 2024 - [c5]Xiang Fu, Andrew S. Rosen, Kyle Bystrom, Rui Wang, Albert Musaelian, Boris Kozinsky, Tess E. Smidt, Tommi S. Jaakkola:
A Recipe for Charge Density Prediction. NeurIPS 2024 - [i14]Xiang Fu, Andrew S. Rosen, Kyle Bystrom, Rui Wang, Albert Musaelian, Boris Kozinsky, Tess E. Smidt, Tommi S. Jaakkola:
A Recipe for Charge Density Prediction. CoRR abs/2405.19276 (2024) - [i13]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:
Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates. CoRR abs/2407.04557 (2024) - 2023
- [j3]Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gómez-Bombarelli, Tommi S. Jaakkola:
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations. Trans. Mach. Learn. Res. 2023 (2023) - [j2]Xiang Fu, Tian Xie, Nathan J. Rebello, Bradley D. Olsen, Tommi S. Jaakkola:
Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-scale Graph Networks. Trans. Mach. Learn. Res. 2023 (2023) - [c4]Gabriel B. Margolis, Xiang Fu, Yandong Ji, Pulkit Agrawal:
Learning to See Physical Properties with Active Sensing Motor Policies. CoRL 2023: 2537-2548 - [i12]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:
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. CoRR abs/2307.08423 (2023) - [i11]Xiang Fu, Tian Xie, Andrew S. Rosen, Tommi S. Jaakkola, Jake Smith:
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design. CoRR abs/2310.10732 (2023) - [i10]Xiang Fu, Albert Musaelian, Anders Johansson, Tommi S. Jaakkola, Boris Kozinsky:
Learning Interatomic Potentials at Multiple Scales. CoRR abs/2310.13756 (2023) - [i9]Gabriel B. Margolis, Xiang Fu, Yandong Ji, Pulkit Agrawal:
Learning to See Physical Properties with Active Sensing Motor Policies. CoRR abs/2311.01405 (2023) - [i8]Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Sasha Shysheya, Jonathan Crabbé, Lixin Sun, Jake Smith, Ryota Tomioka, Tian Xie:
MatterGen: a generative model for inorganic materials design. CoRR abs/2312.03687 (2023) - 2022
- [c3]Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, Tommi S. Jaakkola:
Crystal Diffusion Variational Autoencoder for Periodic Material Generation. ICLR 2022 - [i7]Xiang Fu, Tian Xie, Nathan J. Rebello, Bradley D. Olsen, Tommi S. Jaakkola:
Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning. CoRR abs/2204.10348 (2022) - [i6]Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gómez-Bombarelli, Tommi S. Jaakkola:
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations. CoRR abs/2210.07237 (2022) - 2021
- [j1]Xiang Fu, Shangdi Yu, Austin R. Benson:
Modelling and analysis of tagging networks in Stack Exchange communities. J. Complex Networks 8(5) (2021) - [c2]Gabriel B. Margolis, Tao Chen, Kartik Paigwar, Xiang Fu, Donghyun Kim, Sangbae Kim, Pulkit Agrawal:
Learning to Jump from Pixels. CoRL 2021: 1025-1034 - [c1]Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi S. Jaakkola:
Learning Task Informed Abstractions. ICML 2021: 3480-3491 - [i5]Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi S. Jaakkola:
Learning Task Informed Abstractions. CoRR abs/2106.15612 (2021) - [i4]Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, Tommi S. Jaakkola:
Crystal Diffusion Variational Autoencoder for Periodic Material Generation. CoRR abs/2110.06197 (2021) - [i3]Gabriel B. Margolis, Tao Chen, Kartik Paigwar, Xiang Fu, Donghyun Kim, Sangbae Kim, Pulkit Agrawal:
Learning to Jump from Pixels. CoRR abs/2110.15344 (2021) - [i2]Benson Chen, Xiang Fu, Regina Barzilay, Tommi S. Jaakkola:
Fragment-based Sequential Translation for Molecular Optimization. CoRR abs/2111.01009 (2021)
2010 – 2019
- 2019
- [i1]Xiang Fu, Shangdi Yu, Austin R. Benson:
Modeling and Analysis of Tagging Networks in Stack Exchange Communities. CoRR abs/1902.02372 (2019)
Coauthor Index
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