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
1287 2022 Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds N Thomas, T Smidt, S Kearnes, L Yang, L Li, K Kohlhoff, P Riley
arXiv preprint arXiv:1802.08219, 2018
1100 2018 Design and construction of the MicroBooNE detector R Acciarri, C Adams, R An, A Aparicio, S Aponte, J Asaadi, M Auger, ...
Journal of Instrumentation 12 (02), P02017, 2017
484 2017 Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows K Mathew, JH Montoya, A Faghaninia, S Dwarakanath, M Aykol, H Tang, ...
Computational Materials Science 139, 140-152, 2017
372 2017 Realization of a three-dimensional spin–anisotropic harmonic honeycomb iridate KA Modic, TE Smidt, I Kimchi, NP Breznay, A Biffin, S Choi, RD Johnson, ...
Nature communications 5 (1), 4203, 2014
313 2014 e3nn: Euclidean neural networks M Geiger, T Smidt
arXiv preprint arXiv:2207.09453, 2022
211 2022 Equiformer: Equivariant graph attention transformer for 3d atomistic graphs YL Liao, T Smidt
arXiv preprint arXiv:2206.11990, 2022
211 2022 Proposal for an Electron Antineutrino Disappearance Search Using High-Rate A Bungau, A Adelmann, JR Alonso, W Barletta, R Barlow, L Bartoszek, ...
Physical Review Letters 109 (14), 141802, 2012
170 2012 Sign and basis invariant networks for spectral graph representation learning D Lim, J Robinson, L Zhao, T Smidt, S Sra, H Maron, S Jegelka
arXiv preprint arXiv:2202.13013, 2022
158 2022 Artificial intelligence for science in quantum, atomistic, and continuum systems X Zhang, L Wang, J Helwig, Y Luo, C Fu, Y Xie, M Liu, Y Lin, Z Xu, K Yan, ...
arXiv preprint arXiv:2307.08423, 2023
117 2023 Equiformerv2: Improved equivariant transformer for scaling to higher-degree representations YL Liao, B Wood, A Das, T Smidt
arXiv preprint arXiv:2306.12059, 2023
110 2023 SE (3)-equivariant prediction of molecular wavefunctions and electronic densities O Unke, M Bogojeski, M Gastegger, M Geiger, T Smidt, KR Müller
Advances in Neural Information Processing Systems 34, 14434-14447, 2021
107 2021 Machine learning on neutron and x-ray scattering and spectroscopies Z Chen, N Andrejevic, NC Drucker, T Nguyen, RP Xian, T Smidt, Y Wang, ...
Chemical Physics Reviews 2 (3), 2021
107 2021 Direct prediction of phonon density of states with Euclidean neural networks Z Chen, N Andrejevic, T Smidt, Z Ding, Q Xu, YT Chi, QT Nguyen, A Alatas, ...
Advanced Science 8 (12), 2004214, 2021
94 2021 Relevance of rotationally equivariant convolutions for predicting molecular properties BK Miller, M Geiger, TE Smidt, F Noé
arXiv preprint arXiv:2008.08461, 2020
79 2020 An automatically curated first-principles database of ferroelectrics TE Smidt, SA Mack, SE Reyes-Lillo, A Jain, JB Neaton
Scientific data 7 (1), 72, 2020
68 2020 Euclidean symmetry and equivariance in machine learning TE Smidt
Trends in Chemistry 3 (2), 82-85, 2021
58 2021 Finding symmetry breaking order parameters with euclidean neural networks TE Smidt, M Geiger, BK Miller
Physical Review Research 3 (1), L012002, 2021
58 2021 Expression of interest for a novel search for CP violation in the neutrino sector: DAEdALUS J Alonso, FT Avignone, WA Barletta, R Barlow, HT Baumgartner, ...
arXiv preprint arXiv:1006.0260, 2010
52 2010 Demonstration of a lightguide detector for liquid argon TPCs L Bugel, JM Conrad, C Ignarra, BJP Jones, T Katori, T Smidt, HK Tanaka
Nuclear Instruments and Methods in Physics Research Section A: Accelerators …, 2011
47 2011 
Jeffrey B. NeatonUniversity of California, Berkeley & Lawrence Berkeley National LaboratoryDirección de correo verificada de berkeley.edu