Simon Batzner

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	Hepsi	2019 yılından bugüne
Alıntılar	1663	1659
h-endeksi	11	11
i10-endeksi	11	11

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Finansman sağlayan kuruluşun getirdiği zorunluluğa dayalı olarak

Katkıda bulunan yazarlar

Boris KozinskyHarvard Universityseas.harvard.edu üzerinde doğrulanmış e-posta adresine sahip
Albert MusaelianResearcher, Harvard Universityg.harvard.edu üzerinde doğrulanmış e-posta adresine sahip
Lixin Sun 孙励新Microsoft Research, Cambridge, UKmicrosoft.com üzerinde doğrulanmış e-posta adresine sahip
Jonathan VandermauseD. E. Shaw Researchdeshawresearch.com üzerinde doğrulanmış e-posta adresine sahip
Anders JohanssonPhD studentg.harvard.edu üzerinde doğrulanmış e-posta adresine sahip
Ekin Dogus CubukGoogle DeepMindfas.harvard.edu üzerinde doğrulanmış e-posta adresine sahip
Tess SmidtMassachusetts Institute of Technologymit.edu üzerinde doğrulanmış e-posta adresine sahip
Mario GeigerMITmit.edu üzerinde doğrulanmış e-posta adresine sahip
Cameron J. OwenPhD Candidate @ Harvard Universityg.harvard.edu üzerinde doğrulanmış e-posta adresine sahip
Muratahan AykolGoogle DeepMindgoogle.com üzerinde doğrulanmış e-posta adresine sahip
Amil MerchantGoogle Researchgoogle.com üzerinde doğrulanmış e-posta adresine sahip
Gabor CsanyiProfessor of Molecular Modelling, Engineering Laboratory, University of Cambridgecam.ac.uk üzerinde doğrulanmış e-posta adresine sahip
Ilyes BatatiaPhD Student, University of Cambridgecam.ac.uk üzerinde doğrulanmış e-posta adresine sahip
Ralf DrautzProfessor for Atomistic Modelling and Simulation, ICAMS, Ruhr-Universität Bochumrub.de üzerinde doğrulanmış e-posta adresine sahip

Takip et

Simon Batzner

Diğer adlarSimon Lutz Batzner, Simon L Batzner, S Batzner

Google DeepMind

google.com üzerinde doğrulanmış e-posta adresine sahip - Ana Sayfa


Başlık Alıntılara göre sırala Yıla göre sırala Başlığa göre sırala	Alıntı yapanlar Alıntı yapanlar	Yıl
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	784	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	265	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	240	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	132	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	79	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	31	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	24*	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	24	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	5	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

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