Xiang Fu 个人学术档案 - 学术资源搜索

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	总计	2019 年至今
引用	588	587
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Tommi JaakkolaMIT在 csail.mit.edu 的电子邮件经过验证
Tian XieMicrosoft Research在 microsoft.com 的电子邮件经过验证
Pulkit AgrawalMassachusetts Institute of Technology在 mit.edu 的电子邮件经过验证
Regina BarzilayProfessor of EECS, MIT在 csail.mit.edu 的电子邮件经过验证
Gabriel B. MargolisMassachusetts Institute of Technology在 mit.edu 的电子邮件经过验证
Octavian GaneaMIT, CSAIL在 csail.mit.edu 的电子邮件经过验证
Rafael Gómez-BombarelliAssociate Professor, MIT DMSE在 mit.edu 的电子邮件经过验证
Sinan KetenJerome Cohen Professor, Northwestern University, Mechanical Engineering, Civil & Env. Eng.在 northwestern.edu 的电子邮件经过验证
Wujie WangGenerate Biomedicines, Inc.在 mit.edu 的电子邮件经过验证
Zhenghao WuXi'an Jiaotong-Liverpool University; Northwestern University; TU-Darmstadt在 xjtlu.edu.cn 的电子邮件经过验证
Brad OlsenProfessor of Chemical Engineering, Massachusetts Institute of Technology在 mit.edu 的电子邮件经过验证
Nathan RebelloMassachusetts Institute of Technology在 mit.edu 的电子邮件经过验证
Jake A. SmithMicrosoft Research AI4Science在 microsoft.com 的电子邮件经过验证
Tao ChenPh.D. student, CSAIL, MIT在 mit.edu 的电子邮件经过验证
Donghyun KimUniversity of Massachusetts Amherst在 cs.umass.edu 的电子邮件经过验证
Sangbae KimProfessor of Mechanical Engineering, MIT在 mit.edu 的电子邮件经过验证
Kartik PaigwarGraduate Student, Arizona State University在 asu.edu 的电子邮件经过验证
Ge YangPostdoc Fellow, Massachusetts Institute of Technology在 mit.edu 的电子邮件经过验证
Claudio ZeniSenior Researcher @ Microsoft在 microsoft.com 的电子邮件经过验证
Lixin Sun 孙励新Microsoft Research, Cambridge, UK在 microsoft.com 的电子邮件经过验证

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Xiang Fu

FAIR at Meta

在 meta.com 的电子邮件经过验证 - 首页

Machine Learning Generative Models Molecular Dynamics


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
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	165	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	126	2022
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	69	2021
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	63	2023
Learning Task Informed Abstractions X Fu, G Yang, P Agrawal, T Jaakkola International Conference on Machine Learning (ICML), 3480-3491, 2021	58	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	32	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	27*	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	16	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	11	2020
Fragment-based sequential translation for molecular optimization B Chen, X Fu, R Barzilay, T Jaakkola arXiv preprint arXiv:2111.01009, 2021	8	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	7	2023
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design X Fu, T Xie, AS Rosen, T Jaakkola, J Smith arXiv preprint arXiv:2310.10732, 2023	5	2023
Virtual Node Graph Neural Network for Full Phonon Prediction R Okabe, A Chotrattanapituk, A Boonkird, N Andrejevic, X Fu, ... arXiv preprint arXiv:2301.02197, 2023	1	2023
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		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		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		2023
Learning Interatomic Potentials at Multiple Scales X Fu, A Musaelian, A Johansson, T Jaakkola, B Kozinsky arXiv preprint arXiv:2310.13756, 2023		2023

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