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Modeling molecular potential energy surface is of pivotal importance in science.
Generalized neural-network representation of high-dimensional potential-energy surfaces
Jörg Behler and Michele Parrinello · 2007
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Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, and O. Anatole von Lilienfeld · 2012
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The Concept of the Potential Energy Surface , pages 9–49
Errol G. Lewars · 2016
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Quantum-chemical insights from deep tensor neural networks
K. T. Schutt, F. Arbabzadah, S. Chmiela, K.-R. Muller, and A. Tkatchenko · 2017
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Semi-supervised classification with graph convolutional networks
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Stefan Chmiela, Alexandre Tkatchenko, Huziel E. Sauceda, Igor Poltavsky, Kristof T. Schütt, and Klaus-Robert Müller · 2017
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Tensor field networks: Rotation- and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess E. Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
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End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems
Linfeng Zhang, Jiequn Han, Han Wang, Wissam Saidi, Roberto Car, and Weinan E · 2018
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Moleculenet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, and Vijay Pande · 2018
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Machine learning for potential energy surfaces: An extensive database and assessment of methods
Gunnar Schmitz, Ian Heide Godtliebsen, and Ove Christiansen · 2019
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Cormorant: Covariant molecular neural networks
Brandon M. Anderson, Truong-Son Hy, and Risi Kondor · 2019
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Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Stefan Chmiela, Huziel E. Sauceda, Igor Poltavsky, Klaus-Robert Müller, and Alexandre Tkatchenko · 2019
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Oliver Unke and Markus Meuwly · 2019
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Gauge equivariant convolutional networks and the icosahedral CNN
Taco Cohen, Maurice Weiler, Berkay Kicanaoglu, and Max Welling · 2019
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Directional message passing for molecular graphs
Johannes Klicpera, Janek Groß, and Stephan Günnemann · 2020
Scalars are universal: Equivariant machine learning, structured like classical physics
Soledad Villar, David W Hogg, Kate Storey-Fisher, Weichi Yao, and Ben Blum-Smith · 2021
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Equivariant point cloud analysis via learning orientations for message passing
Shitong Luo, Jiahan Li, Jiaqi Guan, Yufeng Su, Chaoran Cheng, Jian Peng, and Jianzhu Ma · 2021
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Roto-translated local coordinate frames for interacting dynamical systems
Miltiadis Kofinas, Naveen Shankar Nagaraja, and Efstratios Gavves · 2021
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Vector neurons: A general framework for so(3)-equivariant networks
Congyue Deng, Or Litany, Yueqi Duan, Adrien Poulenard, Andrea Tagliasacchi, and Leonidas J. Guibas · 2021
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E(n) equivariant graph neural networks
Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
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Se(3)-transformers: 3d roto-translation equivariant attention networks
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Fast and uncertainty-aware directional message passing for non-equilibrium molecules
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Mesh convolutional neural networks for wall shear stress estimation in 3d artery models
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Equivariant transformers for neural network based molecular potentials
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