Xiang Fu
Xiang Fu
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Crystal diffusion variational autoencoder for periodic material generation
T Xie, X Fu, OE Ganea, R Barzilay, T Jaakkola
The Tenth International Conference on Learning Representations (ICLR), 2021
Forces are not enough: Benchmark and critical evaluation for machine learning force fields with molecular simulations
X Fu, Z Wu, W Wang, T Xie, S Keten, R Gomez-Bombarelli, T Jaakkola
Transactions on Machine Learning Research (TMLR), 2022
Artificial intelligence for science in quantum, atomistic, and continuum systems
X Zhang, L Wang, J Helwig, Y Luo, C Fu, Y Xie, M Liu, Y Lin, Z Xu, K Yan, ...
arXiv preprint arXiv:2307.08423, 2023
Learning to Jump from Pixels
GB Margolis, T Chen, K Paigwar, X Fu, D Kim, S bae Kim, P Agrawal
5th Annual Conference on Robot Learning (CoRL), 2021
Learning Task Informed Abstractions
X Fu, G Yang, P Agrawal, T Jaakkola
International Conference on Machine Learning (ICML), 3480-3491, 2021
Mattergen: a generative model for inorganic materials design
C Zeni, R Pinsler, D Zügner, A Fowler, M Horton, X Fu, S Shysheya, ...
arXiv preprint arXiv:2312.03687, 2023
Simulate time-integrated coarse-grained molecular dynamics with multi-scale graph networks
X Fu, T Xie, NJ Rebello, BD Olsen, T Jaakkola
Transactions on Machine Learning Research (TMLR), 2022
The impact of large language models on scientific discovery: a preliminary study using gpt-4
MR AI4Science, MA Quantum
arXiv preprint arXiv:2311.07361, 2023
Modelling and analysis of tagging networks in Stack Exchange Communities
X Fu, S Yu, AR Benson
Journal of Complex Networks 8 (5), cnz045, 2020
Fragment-based sequential translation for molecular optimization
B Chen, X Fu, R Barzilay, T Jaakkola
arXiv preprint arXiv:2111.01009, 2021
Learning to See Physical Properties with Active Sensing Motor Policies
GB Margolis, X Fu, Y Ji, P Agrawal
Conference on Robot Learning (CoRL), 2023
Mofdiff: Coarse-grained diffusion for metal-organic framework design
X Fu, T Xie, AS Rosen, T Jaakkola, J Smith
The Twelfth International Conference on Learning Representations (ICLR), 2023
Virtual node graph neural network for full phonon prediction
R Okabe, A Chotrattanapituk, A Boonkird, N Andrejevic, X Fu, ...
Nature Computational Science, 1-10, 2024
Thermodynamically Informed Multimodal Learning of High-Dimensional Free Energy Models in Molecular Coarse Graining
BR Duschatko, X Fu, C Owen, Y Xie, A Musaelian, T Jaakkola, B Kozinsky
arXiv preprint arXiv:2405.19386, 2024
Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates
R Okabe, M Cheng, A Chotrattanapituk, NT Hung, X Fu, B Han, Y Wang, ...
arXiv preprint arXiv:2407.04557, 2024
A Recipe for Charge Density Prediction
X Fu, A Rosen, K Bystrom, R Wang, A Musaelian, B Kozinsky, T Smidt, ...
arXiv preprint arXiv:2405.19276, 2024
Latent Space Simulator for Unveiling Molecular Free Energy Landscapes and Predicting Transition Dynamics
S Dobers, H Stark, X Fu, D Beaini, S Günnemann
NeurIPS 2023 AI for Science Workshop, 2023
Learning Interatomic Potentials at Multiple Scales
X Fu, A Musaelian, A Johansson, T Jaakkola, B Kozinsky
NeurIPS 2023 AI for Materials Workshop, 2023
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