Modeling and Analysis of Tagging Networks in Stack Exchange Communities. - Xiang Fu 005; Shangdi Yu; Austin R. Benson (CoRR, 2019)

Learning to Jump from Pixels. - Gabriel B. Margolis; Tao Chen 046; Kartik Paigwar; Xiang Fu 005; Donghyun Kim 002; Sangbae Kim; Pulkit Agrawal 001 (CoRL, 2021)

Learning Task Informed Abstractions. - Xiang Fu 005; Ge Yang 003; Pulkit Agrawal 001; Tommi S. Jaakkola (ICML, 2021)

Modelling and analysis of tagging networks in Stack Exchange communities. - Xiang Fu 005; Shangdi Yu; Austin R. Benson (J. Complex Networks, 2021)

Fragment-based Sequential Translation for Molecular Optimization. - Benson Chen; Xiang Fu 005; Regina Barzilay; Tommi S. Jaakkola (CoRR, 2021)

Crystal Diffusion Variational Autoencoder for Periodic Material Generation. - Tian Xie 002; Xiang Fu 005; Octavian-Eugen Ganea; Regina Barzilay; Tommi S. Jaakkola (ICLR, 2022)

Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning. - Xiang Fu 005; Tian Xie 002; Nathan J. Rebello; Bradley D. Olsen; Tommi S. Jaakkola (CoRR, 2022)

Learning to See Physical Properties with Active Sensing Motor Policies. - Gabriel B. Margolis; Xiang Fu 005; Yandong Ji; Pulkit Agrawal 001 (CoRL, 2023)

Learning Interatomic Potentials at Multiple Scales. - Xiang Fu 005; Albert Musaelian; Anders Johansson; Tommi S. Jaakkola; Boris Kozinsky (CoRR, 2023)

MatterGen: a generative model for inorganic materials design. - Claudio Zeni; Robert Pinsler; Daniel Zügner; Andrew Fowler; Matthew Horton; Xiang Fu 005; Sasha Shysheya; Jonathan Crabbé; Lixin Sun; Jake Smith; Ryota Tomioka; Tian Xie 002 (CoRR, 2023)

Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations. - Xiang Fu 005; Zhenghao Wu; Wujie Wang; Tian Xie 002; Sinan Keten; Rafael Gómez-Bombarelli; Tommi S. Jaakkola (Trans. Mach. Learn. Res., 2023)

Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-scale Graph Networks. - Xiang Fu 005; Tian Xie 002; Nathan J. Rebello; Bradley D. Olsen; Tommi S. Jaakkola (Trans. Mach. Learn. Res., 2023)

MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design. - Xiang Fu 005; Tian Xie 002; Andrew S. Rosen; Tommi S. Jaakkola; Jake Smith (ICLR, 2024)

A Recipe for Charge Density Prediction. - Xiang Fu 005; Andrew S. Rosen; Kyle Bystrom; Rui Wang 086; Albert Musaelian; Boris Kozinsky; Tess E. Smidt; Tommi S. Jaakkola (NeurIPS, 2024)

Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates. - Ryotaro Okabe; Mouyang Cheng; Abhijatmedhi Chotrattanapituk; Nguyen Tuan Hung; Xiang Fu 005; Bowen Han; Yao Wang 017; Weiwei Xie; Robert J. Cava; Tommi S. Jaakkola; Yongqiang Cheng 006; Mingda Li (CoRR, 2024)

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models. - Luis Barroso-Luque; Muhammed Shuaibi; Xiang Fu 005; Brandon M. Wood; Misko Dzamba; Meng Gao; Ammar Rizvi; C. Lawrence Zitnick; Zachary W. Ulissi (CoRR, 2024)

Learning to Model Atoms Across Scales - Xiang Fu 005 (2024)

Virtual node graph neural network for full phonon prediction. - Ryotaro Okabe; Abhijatmedhi Chotrattanapituk; Artittaya Boonkird; Nina Andrejevic; Xiang Fu 005; Tommi S. Jaakkola; Qichen Song; Thanh Nguyen; Nathan C. Drucker; Sai Mu; Yao Wang 017; Bolin Liao; et al. (Nat. Comput. Sci., 2024)

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. - Xuan Zhang; Limei Wang; Jacob Helwig; Youzhi Luo; Cong Fu 003; Yaochen Xie; Meng Liu 015; Yuchao Lin; Zhao Xu 005; Keqiang Yan; Keir Adams; Maurice Weiler; et al. (Found. Trends Mach. Learn., 2025)

Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction. - Xiang Fu 005; Brandon M. Wood; Luis Barroso-Luque; Daniel S. Levine 003; Meng Gao; Misko Dzamba; C. Lawrence Zitnick (CoRR, 2025)

All-atom Diffusion Transformers: Unified generative modelling of molecules and materials. - Chaitanya K. Joshi; Xiang Fu 005; Yi-Lun Liao; Vahe Gharakhanyan; Benjamin Kurt Miller; Anuroop Sriram; Zachary W. Ulissi (CoRR, 2025)

A practical guide to machine learning interatomic potentials - Status and future. - Ryan Jacobs; Dane Morgan; Siamak Attarian; Jun Meng; Chen Shen; Zhenghao Wu; Clare Yijia Xie; Julia H. Yang; Nongnuch Artrith; Ben Blaiszik; Gerbrand Ceder; Kamal Choudhary; et al. (CoRR, 2025)

A generative model for inorganic materials design. - Claudio Zeni; Robert Pinsler; Daniel Zügner; Andrew Fowler; Matthew Horton; Xiang Fu 005; Zilong Wang; Aliaksandra Shysheya; Jonathan Crabbé; Shoko Ueda; Roberto Sordillo; Lixin Sun; et al. (Nat., 2025)

Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching. - Aaron J. Havens; Benjamin Kurt Miller; Bing Yan; Carles Domingo-Enrich; Anuroop Sriram; Brandon M. Wood; Daniel Levine; Bin Hu; Brandon Amos; Brian Karrer; Xiang Fu 005; Guan-Horng Liu; et al. (CoRR, 2025)

UMA: A Family of Universal Models for Atoms. - Brandon M. Wood; Misko Dzamba; Xiang Fu 005; Meng Gao; Muhammed Shuaibi; Luis Barroso-Luque; Kareem Abdelmaqsoud; Vahe Gharakhanyan; John R. Kitchin; Daniel S. Levine 003; Kyle Michel; Anuroop Sriram; et al. (CoRR, 2025)