| E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials S Batzner, A Musaelian, L Sun, M Geiger, JP Mailoa, M Kornbluth, ... Nature communications 13 (1), 2453, 2022 | 1270 | 2022 |
| Scaling deep learning for materials discovery A Merchant, S Batzner, SS Schoenholz, M Aykol, G Cheon, ED Cubuk Nature 624 (7990), 80-85, 2023 | 616 | 2023 |
| Learning local equivariant representations for large-scale atomistic dynamics A Musaelian, S Batzner, A Johansson, L Sun, CJ Owen, M Kornbluth, ... Nature Communications 14 (1), 579, 2023 | 428 | 2023 |
| On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events J Vandermause, SB Torrisi, S Batzner, Y Xie, L Sun, AM Kolpak, ... npj Computational Materials 6 (1), 20, 2020 | 340 | 2020 |
| The design space of E (3)-equivariant atom-centred interatomic potentials I Batatia, S Batzner, DP Kovács, A Musaelian, GNC Simm, R Drautz, ... Nature Machine Intelligence, 1-12, 2025 | 121 | 2025 |
| A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems JP Mailoa, M Kornbluth, S Batzner, G Samsonidze, ST Lam, ... Nature machine intelligence 1 (10), 471-479, 2019 | 60 | 2019 |
| Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size B Kozinsky, A Musaelian, A Johansson, S Batzner Proceedings of the International Conference for High Performance Computing …, 2023 | 49 | 2023 |
| Multitask machine learning of collective variables for enhanced sampling of rare events L Sun, J Vandermause, S Batzner, Y Xie, D Clark, W Chen, B Kozinsky Journal of Chemical Theory and Computation 18 (4), 2341-2353, 2022 | 43 | 2022 |
| Fast uncertainty estimates in deep learning interatomic potentials A Zhu, S Batzner, A Musaelian, B Kozinsky The Journal of Chemical Physics 158 (16), 2023 | 42 | 2023 |
| Advancing molecular simulation with equivariant interatomic potentials S Batzner, A Musaelian, B Kozinsky Nature Reviews Physics 5 (8), 437-438, 2023 | 27 | 2023 |
| Euclidean neural networks: e3nn, 2020 M Geiger, T Smidt, M Alby, BK Miller, W Boomsma, B Dice, K Lapchevskyi, ... URL https://doi. org/10.5281/zenodo 5292912, 0 | 17 | |
| Complexity of many-body interactions in transition metals via machine-learned force fields from the TM23 data set CJ Owen, SB Torrisi, Y Xie, S Batzner, K Bystrom, J Coulter, A Musaelian, ... npj Computational Materials 10 (1), 92, 2024 | 15 | 2024 |
| Accurate Surface and Finite-Temperature Bulk Properties of Lithium Metal at Large Scales Using Machine Learning Interaction Potentials MK Phuthi, AM Yao, S Batzner, A Musaelian, P Guan, B Kozinsky, ... ACS omega 9 (9), 10904-10912, 2024 | 9 | 2024 |
| Transferability and accuracy of ionic liquid simulations with equivariant machine learning interatomic potentials ZAH Goodwin, MB Wenny, JH Yang, A Cepellotti, J Ding, K Bystrom, ... The Journal of Physical Chemistry Letters 15 (30), 7539-7547, 2024 | 8 | 2024 |
| Predicting emergence of crystals from amorphous matter with deep learning M Aykol, A Merchant, S Batzner, JN Wei, ED Cubuk arXiv preprint arXiv:2310.01117, 2023 | 5 | 2023 |
| Biasing energy surfaces towards the unknown S Batzner Nature Computational Science 3 (3), 190-191, 2023 | 2 | 2023 |
| Predicting Properties of Amorphous Solids with Graph Network Potentials M Aykol, JN Wei, S Batzner, A Merchant, ED Cubuk 1st Workshop on the Synergy of Scientific and Machine Learning Modeling …, 2023 | 2 | 2023 |
| Generative Hierarchical Materials Search S Yang, S Batzner, R Gao, M Aykol, AL Gaunt, B McMorrow, DJ Rezende, ... arXiv preprint arXiv:2409.06762, 2024 | | 2024 |
| Chemical Transferability and Accuracy of Ionic Liquid Simulations with Machine Learning Interatomic Potentials ZAH Goodwin, MB Wenny, JH Yang, A Cepellotti, K Bystrom, ... arXiv preprint arXiv:2403.01980, 2024 | | 2024 |
| Equivariant Deep Learning Interatomic Potentials SL Batzner | | 2023 |
Boris KozinskyHarvard University, Bosch ResearchZweryfikowany adres z seas.harvard.edu
