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Point clouds are versatile representations of 3D objects and have found widespread application in science and engineering.
Nearsightedness of electronic matter
E Prodan and W Kohn · 2005
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Nearsightedness of electronic matter
Emil Prodan and Walter Kohn · 2005
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A charge-driven molecular water pump
Xiaojing Gong, Jingyuan Li, Hangjun Lu, Rongzheng Wan, Jichen Li, Jun Hu, and Haiping Fang · 2007
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Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
Jörg Behler and Michele Parrinello · 2007
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High-throughput electronic band structure calculations: Challenges and tools
Wahyu Setyawan and Stefano Curtarolo · 2010
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Static charges cannot drive a continuous flow of water molecules through a carbon nanotube
Jirasak Wong-ekkabut, Markus S. Miettinen, Cristiano Dias, and Mikko Karttunen · 2010
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Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Albert P. Bartók, Mike C. Payne, Risi Kondor, and Gábor Csányi · 2010
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Atom-centered symmetry functions for constructing high-dimensional neural network potentials
Jörg Behler · 2011
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Construction of high-dimensional neural network potentials using environment-dependent atom pairs
K. V.Jovan Jose, Nongnuch Artrith, and Jörg Behler · 2012
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On representing chemical environments
Albert P. Bartók, Risi Kondor, and Gábor Csányi · 2013
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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, and O. Anatole Von Lilienfeld · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Mechanical Properties from Periodic Plane Wave Quantum Mechanical Codes: The Challenge of the Flexible Nanoporous MIL-47(V) Framework
Danny E. P. Vanpoucke, Kurt Lejaeghere, Veronique Van Speybroeck, Michel Waroquier, and An Ghysels · 2015
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Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties
O. Anatole von Lilienfeld, Raghunathan Ramakrishnan, Matthias Rupp, and Aaron Knoll · 2015
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Multi-view convolutional neural networks for 3d shape recognition
Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller · 2015
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Fast and accurate deep network learning by exponential linear units (elus)
Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter · 2015
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Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules
Tristan Bereau, Denis Andrienko, and O. Anatole Von Lilienfeld · 2015
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3d shapenets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
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The good, the bad and the user in soft matter simulations
Jirasak Wong-ekkabut and Mikko Karttunen · 2016
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Ab Initio Quality NMR Parameters in Solid-State Materials Using a High-Dimensional Neural-Network Representation
Jérôme Cuny, Yu Xie, Chris J. Pickard, and Ali A. Hassanali · 2016
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Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials
Alexander V. Shapeev · 2016
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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Geometric Deep Learning: Going beyond Euclidean data
Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
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Unified representation for machine learning of molecules and crystals
Haoyan Huo and Matthias Rupp · 2017
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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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SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
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Multi-view 3d object detection network for autonomous driving
Xiaozhi Chen, Huimin Ma, Ji Wan, Bo Li, and Tian Xia · 2017
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O-cnn: Octree-based convolutional neural networks for 3d shape analysis
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, and Xin Tong · 2017
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Octnet: Learning deep 3d representations at high resolutions
Gernot Riegler, Ali Osman Ulusoy, and Andreas Geiger · 2017
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Escape from cells: Deep kd-networks for the recognition of 3d point cloud models
Roman Klokov and Victor Lempitsky · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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Continuously differentiable exponential linear units
Jonathan T Barron · 2017
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Solvent fluctuations and nuclear quantum effects modulate the molecular hyperpolarizability of water
Chungwen Liang, Gabriele Tocci, David M. Wilkins, Andrea Grisafi, Sylvie Roke, and Michele Ceriotti · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Chemical shifts in molecular solids by machine learning
Federico M. Paruzzo, Albert Hofstetter, Félix Musil, Sandip De, Michele Ceriotti, and Lyndon Emsley · 2018
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Extending the accuracy of the SNAP interatomic potential form
Mitchell A. Wood and Aidan P. Thompson · 2018
Cited alongside, same era.
Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
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Rotationnet: Joint object categorization and pose estimation using multiviews from unsupervised viewpoints
Asako Kanezaki, Yasuyuki Matsushita, and Yoshifumi Nishida · 2018
Cited alongside, same era.
Deep parametric continuous convolutional neural networks
Shenlong Wang, Simon Suo, Wei-Chiu Ma, Andrei Pokrovsky, and Raquel Urtasun · 2018
Cited alongside, same era.
Deep learning using rectified linear units (relu)
Abien Fred Agarap · 2018
Cited alongside, same era.
Paconv: Position adaptive convolution with dynamic kernel assembling on point clouds
Mutian Xu, Runyu Ding, Hengshuang Zhao, and Xiaojuan Qi · 2021
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Point transformer
Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip HS Torr, and Vladlen Koltun · 2021
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Voxel transformer for 3d object detection
Jiageng Mao, Yujing Xue, Minzhe Niu, Haoyue Bai, Jiashi Feng, Xiaodan Liang, Hang Xu, and Chunjing Xu · 2021
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Pct: Point cloud transformer
Meng-Hao Guo, Jun-Xiong Cai, Zheng-Ning Liu, Tai-Jiang Mu, Ralph R Martin, and Shi-Min Hu · 2021
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Frame averaging for invariant and equivariant network design
Omri Puny, Matan Atzmon, Heli Ben-Hamu, Edward J Smith, Ishan Misra, Aditya Grover, and Yaron Lipman · 2021
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Vector neurons: A general framework for so (3)-equivariant networks
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Pointcnn: Convolution on x-transformed points
Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen · 2018
Cited alongside, same era.
Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, and Weinan E · 2018
Cited alongside, same era.
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
Han Wang, Linfeng Zhang, Jiequn Han, and Weinan E · 2018
Cited alongside, same era.
Alchemical and structural distribution based representation for universal quantum machine learning
Felix A. Faber, Anders S. Christensen, Bing Huang, and O. Anatole Von Lilienfeld · 2018
Cited alongside, same era.
Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
Stefan Elfwing, Eiji Uchibe, and Kenji Doya · 2018
Cited alongside, same era.
Dataset: Ab initio thermodynamics of liquid and solid water, 2018
Bingqing Cheng, Edgar Engel, Jörg Behler, Christoph Dellago, and Michele Ceriotti · 2018
Cited alongside, same era.
Machine learning and the physical sciences
Giuseppe Carleo, Ignacio Cirac, Kyle Cranmer, Laurent Daudet, Maria Schuld, Naftali Tishby, Leslie Vogt-Maranto, and Lenka Zdeborová · 2019
Cited alongside, same era.
Congyue Deng, Or Litany, Yueqi Duan, Adrien Poulenard, Andrea Tagliasacchi, and Leonidas J Guibas · 2021
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Systematic generalization with edge transformers
Leon Bergen, Timothy O’Donnell, and Dzmitry Bahdanau · 2021
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High-dimensional neural network potentials for magnetic systems using spin-dependent atom-centered symmetry functions
Marco Eckhoff and Jörg Behler · 2021
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Forcenet: A graph neural network for large-scale quantum calculations
Weihua Hu, Muhammed Shuaibi, Abhishek Das, Siddharth Goyal, Anuroop Sriram, Jure Leskovec, Devi Parikh, and C Lawrence Zitnick · 2021
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Open catalyst 2020 (oc20) dataset and community challenges
Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Riviere, Kevin Tran, Javier Heras-Domingo, Caleb Ho, Weihua Hu, et al · 2021
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E (n) equivariant graph neural networks
Vıctor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
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Simple gnn regularisation for 3d molecular property prediction & beyond
Jonathan Godwin, Michael Schaarschmidt, Alexander Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Veličković, James Kirkpatrick, and Peter Battaglia · 2021
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Spherical message passing for 3d graph networks
Yi Liu, Limei Wang, Meng Liu, Xuan Zhang, Bora Oztekin, and Shuiwang Ji · 2021
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A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
Tsz Wai Ko, Jonas A Finkler, Stefan Goedecker, and Jörg Behler · 2021
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Ultra-fast interpretable machine-learning potentials
Stephen R Xie, Matthias Rupp, and Richard G Hennig · 2021
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Incompleteness of graph neural networks for points clouds in three dimensions
Sergey N Pozdnyakov and Michele Ceriotti · 2022
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Reann: A pytorch-based end-to-end multi-functional deep neural network package for molecular, reactive, and periodic systems
Yaolong Zhang, Junfan Xia, and Bin Jiang · 2022
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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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The design space of E(3)-equivariant atom-centered interatomic potentials
Ilyes Batatia, Simon Batzner, Dávid Péter Kovács, Albert Musaelian, Gregor N. C. Simm, Ralf Drautz, Christoph Ortner, Boris Kozinsky, and Gábor Csányi · 2022
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Rethinking network design and local geometry in point cloud: A simple residual mlp framework
Xu Ma, Can Qin, Haoxuan You, Haoxi Ran, and Yun Fu · 2022
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Pvt: Point-voxel transformer for point cloud learning
Cheng Zhang, Haocheng Wan, Xinyi Shen, and Zizhao Wu · 2022
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Exactly computable and continuous metrics on isometry classes of finite and 1-periodic sequences
Vitaliy Kurlin · 2022
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Computable complete invariants for finite clouds of unlabeled points under euclidean isometry
Vitaliy Kurlin · 2022
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Resolving the data ambiguity for periodic crystals
Daniel Widdowson and Vitaliy Kurlin · 2022
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A smooth basis for atomistic machine learning
Filippo Bigi, Kevin K Huguenin-Dumittan, Michele Ceriotti, and David E Manolopoulos · 2022
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REANN: A PyTorch-based end-to-end multi-functional deep neural network package for molecular, reactive, and periodic systems
Yaolong Zhang, Junfan Xia, and Bin Jiang · 2022
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Tensor-reduced atomic density representations
James P Darby, Dávid P Kovács, Ilyes Batatia, Miguel A Caro, Gus LW Hart, Christoph Ortner, and Gábor Csányi · 2022
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Spherical channels for modeling atomic interactions
Larry Zitnick, Abhishek Das, Adeesh Kolluru, Janice Lan, Muhammed Shuaibi, Anuroop Sriram, Zachary Ulissi, and Brandon Wood · 2022
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Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements
So Takamoto, Chikashi Shinagawa, Daisuke Motoki, Kosuke Nakago, Wenwen Li, Iori Kurata, Taku Watanabe, Yoshihiro Yayama, Hiroki Iriguchi, Yusuke Asano, et al · 2022
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Teanet: Universal neural network interatomic potential inspired by iterative electronic relaxations
So Takamoto, Satoshi Izumi, and Ju Li · 2022
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Equivariant transformers for neural network based molecular potentials
Philipp Thölke and Gianni De Fabritiis · 2022
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Completeness of atomic structure representations
Jigyasa Nigam, Sergey N. Pozdnyakov, Kevin K. Huguenin-Dumittan, and Michele Ceriotti · 2023
Closest in time.
Faenet: Frame averaging equivariant gnn for materials modeling
Alexandre Agm Duval, Victor Schmidt, Alex Hernández-García, Santiago Miret, Fragkiskos D Malliaros, Yoshua Bengio, and David Rolnick · 2023
Closest in time.
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 · 2023
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Wigner kernels: body-ordered equivariant machine learning without a basis
Filippo Bigi, Sergey N. Pozdnyakov, and Michele Ceriotti · 2023
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Modeling high-entropy transition metal alloys with alchemical compression
Nataliya Lopanitsyna, Guillaume Fraux, Maximilian A. Springer, Sandip De, and Michele Ceriotti · 2023
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Dataset: Modeling high-entropy transition-metal alloys with alchemical compression: Dataset HEA25, April 2023
Nataliya Lopanitsyna, Guillaume Fraux, Maximilian A. Springer, Sandip De, and Michele Ceriotti · 2023
Closest in time.
Fast general two-and three-body interatomic potential
Sergey Pozdnyakov, Artem R Oganov, Efim Mazhnik, Arslan Mazitov, and Ivan Kruglov · 2023
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