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Graph neural networks (GNNs) have been applied to a large variety of applications in materials science and chemistry.
Some properties of line digraphs
Frank Harary and Robert Z Norman · 1960
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Special points for brillouin-zone integrations
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Directional message passing for molecular graphs
Johannes Klicpera, Janek Groß, and Stephan Günnemann · 2003
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Directional message passing for molecular graphs
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Fast and uncertainty-aware directional message passing for non-equilibrium molecules
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New cubic perovskites for one- and two-photon water splitting using the computational materials repository
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Convolutional networks on graphs for learning molecular fingerprints
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The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
Scott Kirklin, James E Saal, Bryce Meredig, Alex Thompson, Jeff W Doak, Muratahan Aykol, Stephan Rühl, and Chris Wolverton · 2015
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Materials cartography: representing and mining materials space using structural and electronic fingerprints
Olexandr Isayev, Denis Fourches, Eugene N Muratov, Corey Oses, Kevin Rasch, Alexander Tropsha, and Stefano Curtarolo · 2015
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ImageNet Large Scale Visual Recognition Challenge
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Charting the complete elastic properties of inorganic crystalline compounds
Maarten de Jong, Wei Chen, Thomas Angsten, Anubhav Jain, Randy Notestine, Anthony Gamst, Marcel Sluiter, Chaitanya Krishna Ande, Sybrand van der Zwaag, Jose J Plata, Cormac Toher, Stefano Curtarolo, Gerbrand Ceder, Kristin A. Persson, and Mark Asta · 2015
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Metal–organic frameworks as platforms for functional materials
Yuanjing Cui, Bin Li, Huajun He, Wei Zhou, Banglin Chen, and Guodong Qian · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 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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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 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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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling · 2018
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Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Tian Xie and Jeffrey C Grossman · 2018
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Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
Gemnet: Universal directional graph neural networks for molecules
Johannes Klicpera, Florian Becker, and Stephan Günnemann · 2021
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Atomistic line graph neural network for improved materials property predictions
Kamal Choudhary and Brian DeCost · 2021
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A geometric-information-enhanced crystal graph network for predicting properties of materials
Jiucheng Cheng, Chunkai Zhang, and Lifeng Dong · 2021
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Gemnet: Universal directional graph neural networks for molecules
Johannes Gasteiger, Florian Becker, and Stephan Günnemann · 2021
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Tim Hsu, Nathan Keilbart, Stephen Weitzner, James Chapman, Penghao Xiao, Tuan Anh Pham, S Roger Qiu, Xiao Chen, and Brandon C Wood · 2021
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Matminer: An open source toolkit for materials data mining
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Kamal Choudhary, Brian DeCost, and Francesca Tavazza · 2018
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Crystal graph neural networks for data mining in materials science
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Optuna: A next-generation hyperparameter optimization framework
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Orbital graph convolutional neural network for material property prediction
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Graph neural networks in tensorflow-keras with raggedtensor representation (kgcnn)
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Graphnorm: A principled approach to accelerating graph neural network training
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Equivariant message passing for the prediction of tensorial properties and molecular spectra
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The open catalyst 2022 (oc22) dataset and challenges for oxide electrocatalysis
Richard Tran, Janice Lan, Muhammed Shuaibi, Brandon Wood, Siddharth Goyal, Abhishek Das, Javier Heras-Domingo, Adeesh Kolluru, Ammar Rizvi, Nima Shoghi, Anuroop Sriram, Zachary Ulissi, and C. Lawrence Zitnick · 2022
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E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
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A universal graph deep learning interatomic potential for the periodic table
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Periodic graph transformers for crystal material property prediction
Keqiang Yan, Yi Liu, Yuchao Lin, and Shuiwang Ji · 2022
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High-throughput identification and characterization of two-dimensional materials using density functional theory
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The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design
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Benchmarking materials property prediction methods: the matbench test set and automatminer reference algorithm
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Materials property prediction for limited datasets enabled by feature selection and joint learning with MODNet
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A critical examination of robustness and generalizability of machine learning prediction of materials properties
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