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Rotation equivariant graph neural networks, i.e.
Pracniques: further remarks on reducing truncation errors
W. Kahan · 1965
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Roofline: an insightful visual performance model for multicore architectures
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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QuTiP 2: A Python framework for the dynamics of open quantum systems
J.R. Johansson, P.D. Nation, and F. Nori · 2012
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Tensor-matrix products with a compressed sparse tensor
Shaden Smith and George Karypis · 2015
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Clebsch–Gordan nets: a fully Fourier space spherical convolutional neural network
Risi Kondor, Zhen Lin, and Shubhendu Trivedi · 2018
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Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
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3D steerable cnns: learning rotationally equivariant features in volumetric data
Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, and Taco Cohen · 2018
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Design principles for sparse matrix multiplication on the gpu
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Cormorant: covariant molecular neural networks
Brandon Anderson, Truong-Son Hy, and Risi Kondor · 2019
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NVIDIA A100 tensor core GPU architecture
NVIDIA Corporation · 2020
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SE(3)-transformers: 3d roto-translation equivariant attention networks
Fabian Fuchs, Daniel Worrall, Volker Fischer, and Max Welling · 2020
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ALTO: adaptive linearized storage of sparse tensors
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Highly accurate protein structure prediction with AlphaFold
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Physics-informed machine learning
George Em Karniadakis, Ioannis G. Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang · 2021
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Equivariant message passing for the prediction of tensorial properties and molecular spectra
Kristof Schütt, Oliver Unke, and Michael Gastegger · 2021
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MACE: Higher order equivariant message passing neural networks for fast and accurate force fields
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Mario Geiger, Emine Kucukbenli, Becca Zandstein, and Kyle Tretina · 2024
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e3nn.c, November 2024
Teddy Koker · 2024
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Risi Kondor and Erik Henning Thiede · 2024
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QuTiP 5: The quantum toolbox in Python, 2024
Neill Lambert, Eric Giguère, Paul Menczel, Boxi Li, Patrick Hopf, Gerardo Suárez, Marc Gali, Jake Lishman, Rushiraj Gadhvi, Rochisha Agarwal, Asier Galicia, Nathan Shammah, Paul D. Nation, J. R. Johansson, Shahnawaz Ahmed, Simon Cross, Alexander Pitchford, and Franco Nori · 2024
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Scaling computational performance of spherical harmonics kernels with Triton
Kin Long Kelvin Lee, Mikhail Galkin, and Santiago Miret · 2024
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Ilyes Batatia, David Peter Kovacs, Gregor N. C. Simm, Christoph Ortner, and Gabor Csanyi · 2022
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e3nn: Euclidean neural networks, 2022
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Euclidean neural networks: e3nn, April 2022
Mario Geiger, Tess Smidt, Alby M., Benjamin Kurt Miller, Wouter Boomsma, Bradley Dice, Kostiantyn Lapchevskyi, Maurice Weiler, Michał Tyszkiewicz, Simon Batzner, Dylan Madisetti, Martin Uhrin, Jes Frellsen, Nuri Jung, Sophia Sanborn, Mingjian Wen, Josh Rackers, Marcel Rød, and Michael Bailey · 2022
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Fast evaluation of spherical harmonics with sphericart
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Diffdock: Diffusion steps, twists, and turns for molecular docking
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Equiformer: Equivariant graph attention transformer for 3D atomistic graphs
Yi-Lun Liao and Tess Smidt · 2023
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What is… an equivariant neural network?
Lek-Heng Lim and Bradley J Nelson · 2023
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Reducing SO(3) convolutions to SO(2) for efficient equivariant GNNs
Saro Passaro and C. Lawrence Zitnick · 2023
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Enabling efficient equivariant operations in the Fourier basis via gaunt tensor products
Shengjie Luo, Tianlang Chen, and Aditi S. Krishnapriyan · 2024
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E3x: E ( 3 ) \mathrm{E}(3) -equivariant deep learning made easy, 2024
Oliver T. Unke and Hartmut Maennel · 2024
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The price of freedom: Exploring tradeoffs between expressivity and computational efficiency in equivariant tensor products
YuQing Xie, Ameya Daigavane, Mit Kotak, and Tess Smidt · 2024
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High-performance training and inference for deep equivariant interatomic potentials, 2025
Chuin Wei Tan, Marc L. Descoteaux, Mit Kotak, Gabriel de Miranda Nascimento, Seán R. Kavanagh, Laura Zichi, Menghang Wang, Aadit Saluja, Yizhong R. Hu, Tess Smidt, Anders Johansson, William C. Witt, Boris Kozinsky, and Albert Musaelian · 2025
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E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E. Smidt, and Boris Kozinsky · 2041
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Learning local equivariant representations for large-scale atomistic dynamics
Albert Musaelian, Simon Batzner, Anders Johansson, Lixin Sun, Cameron J. Owen, Mordechai Kornbluth, and Boris Kozinsky · 2041
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Higher-order equivariant neural networks for charge density prediction in materials
Teddy Koker, Keegan Quigley, Eric Taw, Kevin Tibbetts, and Lin Li · 2057
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