| 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 | 1246 | 2022 |
| 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 | 413 | 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 | 118 | 2022 |
| Evolution of metastable structures at bimetallic surfaces from microscopy and machine-learning molecular dynamics JS Lim, J Vandermause, MA Van Spronsen, A Musaelian, Y Xie, L Sun, ... Journal of the American Chemical Society 142 (37), 15907-15916, 2020 | 69 | 2020 |
| 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 | 48 | 2023 |
| Unsupervised landmark analysis for jump detection in molecular dynamics simulations L Kahle, A Musaelian, N Marzari, B Kozinsky Physical Review Materials 3 (5), 055404, 2019 | 41 | 2019 |
| Fast uncertainty estimates in deep learning interatomic potentials A Zhu, S Batzner, A Musaelian, B Kozinsky The Journal of Chemical Physics 158 (16), 2023 | 40 | 2023 |
| Euclidean neural networks: e3nn M Geiger, T Smidt, M Alby, BK Miller, W Boomsma, B Dice, K Lapchevskyi, ... Version 0.5. 0, 2022 | 29 | 2022 |
| Advancing molecular simulation with equivariant interatomic potentials S Batzner, A Musaelian, B Kozinsky Nature Reviews Physics 5 (8), 437-438, 2023 | 26 | 2023 |
| 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 | 8 | 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 | 6 | 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 | 5 | 2024 |
| Unified Differentiable Learning of the Electric Enthalpy and Dielectric Properties with Exact Physical Constraints S Falletta, A Cepellotti, CW Tan, A Johansson, A Musaelian, CJ Owen, ... arXiv preprint arXiv:2403.17207, 2024 | 3 | 2024 |
| Learning Interatomic Potentials at Multiple Scales X Fu, A Musaelian, A Johansson, T Jaakkola, B Kozinsky arXiv preprint arXiv:2310.13756, 2023 | 2 | 2023 |
| Atomistic evolution of active sites in multi-component heterogeneous catalysts CJ Owen, L Russotto, CR O'Connor, N Marcella, A Johansson, ... arXiv preprint arXiv:2407.13607, 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 |
| 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 |
Boris KozinskyHarvard University, Bosch ResearchVerified email at seas.harvard.edu
