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Developing equivariant neural networks for the E(3) group plays an important role in modeling 3D data across real-world applications.
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Machine learning of accurate energy-conserving molecular force fields
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Steerable CNNs
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Spherenet: Learning spherical representations for detection and classification in omnidirectional images
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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 · 2022
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Geometric and physical quantities improve e(3) equivariant message passing
Johannes Brandstetter, Rob Hesselink, Elise van der Pol, Erik J Bekkers, and Max Welling · 2022
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Atomic cluster expansion: Completeness, efficiency and stability
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So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems
Thorben Frank, Oliver Unke, and Klaus-Robert Müller · 2022
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Gemnet-oc: developing graph neural networks for large and diverse molecular simulation datasets
Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, Stephan Günnemann, Zachary Ward Ulissi, C Lawrence Zitnick, and Abhishek Das · 2022
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3d steerable cnns: Learning rotationally equivariant features in volumetric data
Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, and Taco S Cohen · 2018
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sgdml: Constructing accurate and data efficient molecular force fields using machine learning
Stefan Chmiela, Huziel E Sauceda, Igor Poltavsky, Klaus-Robert Müller, and Alexandre Tkatchenko · 2019
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Atomic cluster expansion for accurate and transferable interatomic potentials
Ralf Drautz · 2019
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Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data
Mikaela Angelina Uy, Quang-Hieu Pham, Binh-Son Hua, Thanh Nguyen, and Sai-Kit Yeung · 2019
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Generalizing convolutional neural networks for equivariance to lie groups on arbitrary continuous data
Marc Finzi, Samuel Stanton, Pavel Izmailov, and Andrew Gordon Wilson · 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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Geometric deep learning on molecular representations
Kenneth Atz, Francesca Grisoni, and Gisbert Schneider · 2021
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e3nn: Euclidean neural networks
Mario Geiger and Tess Smidt · 2022
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Newtonnet: A newtonian message passing network for deep learning of interatomic potentials and forces
Mojtaba Haghighatlari, Jie Li, Xingyi Guan, Oufan Zhang, Akshaya Das, Christopher J Stein, Farnaz Heidar-Zadeh, Meili Liu, Martin Head-Gordon, Luke Bertels, et al · 2022
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Geometrically equivariant graph neural networks: A survey
Jiaqi Han, Yu Rong, Tingyang Xu, and Wenbing Huang · 2022
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nabladft: Large-scale conformational energy and hamiltonian prediction benchmark and dataset
Kuzma Khrabrov, Ilya Shenbin, Alexander Ryabov, Artem Tsypin, Alexander Telepov, Anton Alekseev, Alexander Grishin, Pavel Strashnov, Petr Zhilyaev, Sergey Nikolenko, et al · 2022
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Adsorbml: Accelerating adsorption energy calculations with machine learning
Janice Lan, Aini Palizhati, Muhammed Shuaibi, Brandon M Wood, Brook Wander, Abhishek Das, Matt Uyttendaele, C Lawrence Zitnick, and Zachary W Ulissi · 2022
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Deep-learning density functional theory hamiltonian for efficient ab initio electronic-structure calculation
He Li, Zun Wang, Nianlong Zou, Meng Ye, Runzhang Xu, Xiaoxun Gong, Wenhui Duan, and Yong Xu · 2022
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Unified theory of atom-centered representations and message-passing machine-learning schemes
Jigyasa Nigam, Sergey Pozdnyakov, Guillaume Fraux, and Michele Ceriotti · 2022
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Benchmarking graphormer on large-scale molecular modeling datasets
Yu Shi, Shuxin Zheng, Guolin Ke, Yifei Shen, Jiacheng You, Jiyan He, Shengjie Luo, Chang Liu, Di He, and Tie-Yan Liu · 2022
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Equivariant transformers for neural network based molecular potentials
Philipp Thölke and Gianni De Fabritiis · 2022
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Spherical channels for modeling atomic interactions
C. Lawrence Zitnick, Abhishek Das, Adeesh Kolluru, Janice Lan, Muhammed Shuaibi, Anuroop Sriram, Zachary Ward Ulissi, and Brandon M Wood · 2022
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GeoMFormer: A general architecture for geometric molecular representation learning
Tianlang Chen, Shengjie Luo, Di He, Shuxin Zheng, Tie-Yan Liu, and Liwei Wang · 2023
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A hitchhiker’s guide to geometric gnns for 3d atomic systems
Alexandre Duval, Simon V Mathis, Chaitanya K Joshi, Victor Schmidt, Santiago Miret, Fragkiskos D Malliaros, Taco Cohen, Pietro Lio, Yoshua Bengio, and Michael Bronstein · 2023
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General framework for e (3)-equivariant neural network representation of density functional theory hamiltonian
Xiaoxun Gong, He Li, Nianlong Zou, Runzhang Xu, Wenhui Duan, and Yong Xu · 2023
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On the expressive power of geometric graph neural networks
Chaitanya K. Joshi, Cristian Bodnar, Simon V Mathis, Taco Cohen, and Pietro Lio · 2023
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Evaluation of the MACE force field architecture: From medicinal chemistry to materials science
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Equiformer: Equivariant graph attention transformer for 3d atomistic graphs
Yi-Lun Liao and Tess Smidt · 2023
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One transformer can understand both 2d & 3d molecular data
Shengjie Luo, Tianlang Chen, Yixian Xu, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, and Di He · 2023
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Learning local equivariant representations for large-scale atomistic dynamics
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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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Tensornet: Cartesian tensor representations for efficient learning of molecular potentials
Guillem Simeon and Gianni De Fabritiis · 2023
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Spatial attention kinetic networks with e(n)-equivariance
Yuanqing Wang and John Chodera · 2023
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Equiformerv2: Improved equivariant transformer for scaling to higher-degree representations
Yi-Lun Liao, Brandon Wood, Abhishek Das, and Tess Smidt · 2024
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