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Generating the periodic structure of stable materials is a long-standing challenge for the material design community.
Three dimensional nets and polyhedra
Alexander Frank Wells et al · 1977
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Plane nets in crystal chemistry
M. O’Keeffe and B. G. Hyde · 1980
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New developments in the inorganic crystal structure database (icsd): accessibility in support of materials research and design
Alec Belsky, Mariette Hellenbrandt, Vicky Lynn Karen, and Peter Luksch · 2002
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Numerically stable algorithms for the computation of reduced unit cells
Ralf W Grosse-Kunstleve, Nicholas K Sauter, and Paul D Adams · 2004
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Zinc- a free database of commercially available compounds for virtual screening
John J Irwin and Brian K Shoichet · 2005
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Uspex—evolutionary crystal structure prediction
Colin W Glass, Artem R Oganov, and Nikolaus Hansen · 2006
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High-pressure phases of silane
Chris J Pickard and RJ Needs · 2006
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Data mined ionic substitutions for the discovery of new compounds
Geoffroy Hautier, Chris Fischer, Virginie Ehrlacher, Anubhav Jain, and Gerbrand Ceder · 2011
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Fast and uncertainty-aware directional message passing for non-equilibrium molecules
Johannes Klicpera, Shankari Giri, Johannes T Margraf, and Stephan Günnemann · 2011
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Ab initio random structure searching
Chris J Pickard and RJ Needs · 2011
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Calypso: A method for crystal structure prediction
Yanchao Wang, Jian Lv, Li Zhu, and Yanming Ma · 2012
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Commentary: The materials project: A materials genome approach to accelerating materials innovation
Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, et al · 2013
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Python materials genomics (pymatgen): A robust, open-source python library for materials analysis
Shyue Ping Ong, William Davidson Richards, Anubhav Jain, Geoffroy Hautier, Michael Kocher, Shreyas Cholia, Dan Gunter, Vincent L Chevrier, Kristin A Persson, and Gerbrand Ceder · 2013
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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A general-purpose machine learning framework for predicting properties of inorganic materials
Logan Ward, Ankit Agrawal, Alok Choudhary, and Christopher Wolverton · 2016
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Machine learning for molecular and materials science
Keith T Butler, Daniel W Davies, Hugh Cartwright, Olexandr Isayev, and Aron Walsh · 2018
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Data-driven learning of total and local energies in elemental boron
Volker L Deringer, Chris J Pickard, and Gábor Csányi · 2018
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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Schnet–a deep learning architecture for molecules and materials
Kristof T Schütt, Huziel E Sauceda, P-J Kindermans, Alexandre Tkatchenko, and K-R Müller · 2018
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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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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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Graph networks as a universal machine learning framework for molecules and crystals
Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, and Shyue Ping Ong · 2019
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Smact: Semiconducting materials by analogy and chemical theory
Daniel W Davies, Keith T Butler, Adam J Jackson, Jonathan M Skelton, Kazuki Morita, and Aron Walsh · 2019
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Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Niklas Gebauer, Michael Gastegger, and Kristof Schütt · 2019
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Data-driven approach to encoding and decoding 3-d crystal structures
Jordan Hoffmann, Louis Maestrati, Yoshihide Sawada, Jian Tang, Jean Michel Sellier, and Yoshua Bengio · 2019
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Inverse design of solid-state materials via a continuous representation
Juhwan Noh, Jaehoon Kim, Helge S Stein, Benjamin Sanchez-Lengeling, John M Gregoire, Alan Aspuru-Guzik, and Yousung Jung · 2019
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alex Nichol · 2021
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Geomol: Torsional geometric generation of molecular 3d conformer ensembles
Octavian-Eugen Ganea, Lagnajit Pattanaik, Connor W Coley, Regina Barzilay, Klavs F Jensen, William H Green, and Tommi S Jaakkola · 2021
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Inverse design of 3d molecular structures with conditional generative neural networks
Niklas WA Gebauer, Michael Gastegger, Stefaan SP Hessmann, Klaus-Robert Müller, and Kristof T Schütt · 2021
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Very deep graph neural networks via noise regularisation
Jonathan Godwin, Michael Schaarschmidt, Alexander Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Veličković, James Kirkpatrick, and Peter Battaglia · 2021
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Structure prediction drives materials discovery
Artem R Oganov, Chris J Pickard, Qiang Zhu, and Richard J Needs · 2019
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Study of deep generative models for inorganic chemical compositions
Yoshihide Sawada, Koji Morikawa, and Mikiya Fujii · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Pointflow: 3d point cloud generation with continuous normalizing flows
Guandao Yang, Xun Huang, Zekun Hao, Ming-Yu Liu, Serge Belongie, and Bharath Hariharan · 2019
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Learning gradient fields for shape generation
Ruojin Cai, Guandao Yang, Hadar Averbuch-Elor, Zekun Hao, Serge Belongie, Noah Snavely, and Bharath Hariharan · 2020
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3-d inorganic crystal structure generation and property prediction via representation learning
Callum J Court, Batuhan Yildirim, Apoorv Jain, and Jacqueline M Cole · 2020
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Generative adversarial networks (gan) based efficient sampling of chemical composition space for inverse design of inorganic materials
Yabo Dan, Yong Zhao, Xiang Li, Shaobo Li, Ming Hu, and Jianjun Hu · 2020
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Contact map based crystal structure prediction using global optimization
Jianjun Hu, Wenhui Yang, Rongzhi Dong, Yuxin Li, Xiang Li, Shaobo Li, and Edirisuriya MD Siriwardane · 2021
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E (n) equivariant normalizing flows for molecule generation in 3d
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