| 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 | 295 | 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 | 220 | 2022 |
| A generative model for inorganic materials design C Zeni, R Pinsler, D Zügner, A Fowler, M Horton, X Fu, Z Wang, ... Nature, 1-3, 2025 | 156* | 2025 |
| 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 | 139 | 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 | 89 | 2021 |
| Learning Task Informed Abstractions X Fu, G Yang, P Agrawal, T Jaakkola International Conference on Machine Learning (ICML), 3480-3491, 2021 | 76 | 2021 |
| 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 | 45 | 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 | 45* | 2022 |
| Open materials 2024 (omat24) inorganic materials dataset and models L Barroso-Luque, M Shuaibi, X Fu, BM Wood, M Dzamba, M Gao, A Rizvi, ... arXiv preprint arXiv:2410.12771, 2024 | 31 | 2024 |
| 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 | 19 | 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 | 16 | 2020 |
| Learning to See Physical Properties with Active Sensing Motor Policies GB Margolis, X Fu, Y Ji, P Agrawal Conference on Robot Learning (CoRL), 2023 | 15 | 2023 |
| Virtual node graph neural network for full phonon prediction R Okabe, A Chotrattanapituk, A Boonkird, N Andrejevic, X Fu, ... Nature Computational Science 4 (7), 522-531, 2024 | 13 | 2024 |
| Fragment-based sequential translation for molecular optimization B Chen, X Fu, R Barzilay, T Jaakkola arXiv preprint arXiv:2111.01009, 2021 | 9 | 2021 |
| 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 | 7 | 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 | 3 | 2024 |
| Learning Interatomic Potentials at Multiple Scales X Fu, A Musaelian, A Johansson, T Jaakkola, B Kozinsky NeurIPS 2023 AI for Materials Workshop, 2023 | 3 | 2023 |
| A recipe for charge density prediction X Fu, A Rosen, K Bystrom, R Wang, A Musaelian, B Kozinsky, T Smidt, ... Advances in Neural Information Processing Systems 37, 9727-9752, 2024 | 2 | 2024 |
| A practical guide to machine learning interatomic potentials–Status and future R Jacobs, D Morgan, S Attarian, J Meng, C Shen, Z Wu, CY Xie, JH Yang, ... Current Opinion in Solid State and Materials Science 35, 101214, 2025 | 1 | 2025 |
| 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 | 1 | 2023 |
Tommi JaakkolaMITVerified email at csail.mit.edu
