| 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 | 776 | 2022 |
| 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 | 264 | 2020 |
| 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 | 236 | 2023 |
| Scaling deep learning for materials discovery A Merchant, S Batzner, SS Schoenholz, M Aykol, G Cheon, ED Cubuk Nature 624 (7990), 80-85, 2023 | 126 | 2023 |
| The design space of e (3)-equivariant atom-centered interatomic potentials I Batatia, S Batzner, DP Kovács, A Musaelian, GNC Simm, R Drautz, ... arXiv preprint arXiv:2205.06643, 2022 | 76 | 2022 |
| 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 | 49 | 2019 |
| 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 | 30 | 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 | 24 | 2023 |
| 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 | 23* | 2023 |
| Advancing molecular simulation with equivariant interatomic potentials S Batzner, A Musaelian, B Kozinsky Nature Reviews Physics 5 (8), 437-438, 2023 | 16 | 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 | 12 | |
| 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, ... arXiv preprint arXiv:2302.12993, 2023 | 4 | 2023 |
| 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, 2024 | 1 | 2024 |
| 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 | 1 | 2023 |
| 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 |
| 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 | | 2023 |
| Equivariant Deep Learning Interatomic Potentials SL Batzner | | 2023 |
| Biasing energy surfaces towards the unknown S Batzner Nature Computational Science 3 (3), 190-191, 2023 | | 2023 |
| Learning symmetry-preserving interatomic force fields for atomistic simulations SL Batzner Massachusetts Institute of Technology, 2019 | | 2019 |
Boris KozinskyHarvard UniversityVerified email at seas.harvard.edu
