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Generative models hold the promise of significantly expediting the materials design process when compared to traditional human-guided or rule-based methodologies.
The principles determining the structure of complex ionic crystals
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Convolutional networks for images, speech, and time series
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Computational screening of perovskite metal oxides for optimal solar light capture
Ivano E Castelli, Thomas Olsen, Soumendu Datta, David D Landis, Søren Dahl, Kristian S Thygesen, and Karsten W Jacobsen · 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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Predicting crystal structures of organic compounds
Sarah L Price · 2014
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Retracted chapter: On the theory of stochastic processes, with particular reference to applications
William Feller · 2015
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The chemical space project
Jean-Louis Reymond · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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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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Computational materials design of crystalline solids
Keith T Butler, Jarvist M Frost, Jonathan M Skelton, Katrine L Svane, and Aron Walsh · 2016
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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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The 2019 materials by design roadmap
Kirstin Alberi, Marco Buongiorno Nardelli, Andriy Zakutayev, Lubos Mitas, Stefano Curtarolo, Anubhav Jain, Marco Fornari, Nicola Marzari, Ichiro Takeuchi, Martin L Green, et al · 2018
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Molgan: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 2018
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Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik · 2018
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Graphvae: Towards generation of small graphs using variational autoencoders
Martin Simonovsky and Nikos Komodakis · 2018
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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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New frontiers for the materials genome initiative
Juan J de Pablo, Nicholas E Jackson, Michael A Webb, Long-Qing Chen, Joel E Moore, Dane Morgan, Ryan Jacobs, Tresa Pollock, Darrell G Schlom, Eric S Toberer, et al · 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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Machine-enabled inverse design of inorganic solid materials: promises and challenges
Juhwan Noh, Geun Ho Gu, Sungwon Kim, and Yousung Jung · 2020
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Airss data for carbon at 10gpa and the c+ n+ h+ o system at 1gpa
Chris J Pickard · 2020
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Inverse design of crystals using generalized invertible crystallographic representation
Zekun Ren, Juhwan Noh, Siyu Tian, Felipe Oviedo, Guangzong Xing, Qiaohao Liang, Armin Aberle, Yi Liu, Qianxiao Li, Senthilnath Jayavelu, et al · 2020
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Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity
Nils ER Zimmermann and Anubhav Jain · 2020
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Data-driven approach to encoding and decoding 3-d crystal structures
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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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Structure prediction drives materials discovery
Artem R Oganov, Chris J Pickard, Qiang Zhu, and Richard J Needs · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Embedded atom neural network potentials: Efficient and accurate machine learning with a physically inspired representation
Yaolong Zhang, Ce Hu, and Bin Jiang · 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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Diffusion probabilistic models for 3d point cloud generation
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Physics-inspired structural representations for molecules and materials
Felix Musil, Andrea Grisafi, Albert P Bartók, Christoph Ortner, Gábor Csányi, and Michele Ceriotti · 2021
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Learning gradient fields for molecular conformation generation
Chence Shi, Shitong Luo, Minkai Xu, and Jian Tang · 2021
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Through the eyes of a descriptor: Constructing complete, invertible descriptions of atomic environments
Martin Uhrin · 2021
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Crystal diffusion variational autoencoder for periodic material generation
Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, and Tommi Jaakkola · 2021
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A survey on generative diffusion model
Hanqun Cao, Cheng Tan, Zhangyang Gao, Yilun Xu, Guangyong Chen, Pheng-Ann Heng, and Stan Z Li · 2022
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Atomic structure generation from reconstructing structural fingerprints
Victor Fung, Shuyi Jia, Jiaxin Zhang, Sirui Bi, Junqi Yin, and P Ganesh · 2022
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Diffusion models in vision: A survey
Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah · 2023
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Daniel Flam-Shepherd and Alán Aspuru-Guzik · 2023
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Towards symmetry-aware generation of periodic materials
Youzhi Luo, Chengkai Liu, and Shuiwang Ji · 2023
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