Simon Batzner
Simon Batzner
Other namesSimon Lutz Batzner, Simon L Batzner, S Batzner
Google DeepMind
Verified email at - Homepage
Cited by
Cited by
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
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
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
Scaling deep learning for materials discovery
A Merchant, S Batzner, SS Schoenholz, M Aykol, G Cheon, ED Cubuk
Nature 624 (7990), 80-85, 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
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
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
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
Fast uncertainty estimates in deep learning interatomic potentials
A Zhu, S Batzner, A Musaelian, B Kozinsky
The Journal of Chemical Physics 158 (16), 2023
Advancing molecular simulation with equivariant interatomic potentials
S Batzner, A Musaelian, B Kozinsky
Nature Reviews Physics 5 (8), 437-438, 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
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
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
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
Biasing energy surfaces towards the unknown
S Batzner
Nature Computational Science 3 (3), 190-191, 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
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
Equivariant Deep Learning Interatomic Potentials
SL Batzner
Learning symmetry-preserving interatomic force fields for atomistic simulations
SL Batzner
Massachusetts Institute of Technology, 2019
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Articles 1–19