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We consider the problem of generating periodic materials with deep models.
Group theory: application to the physics of condensed matter
Mildred S Dresselhaus, Gene Dresselhaus, and Ado Jorio · 2007
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A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
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Bayesian learning via stochastic gradient Langevin dynamics
Max Welling and Yee Whye Teh · 2011
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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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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Auto-Encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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Group theory in a nutshell for physicists , volume 17
Anthony Zee · 2016
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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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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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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 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 convolutional policy network for goal-directed molecular graph generation
Jiaxuan You, Bowen Liu, Zhitao Ying, Vijay Pande, and Jure Leskovec · 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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Molecular geometry prediction using a deep generative graph neural network
Elman Mansimov, Omar Mahmood, Seokho Kang, and Kyunghyun Cho · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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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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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Generative adversarial networks for crystal structure prediction
Sungwon Kim, Juhwan Noh, Geun Ho Gu, Alan Aspuru-Guzik, and Yousung Jung · 2020
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Deep learning enabled inorganic material generator
Learning gradient fields for molecular conformation generation
Chence Shi, Shitong Luo, Minkai Xu, and Jian Tang · 2021
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
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High-throughput discovery of novel cubic crystal materials using deep generative neural networks
Yong Zhao, Mohammed Al-Fahdi, Ming Hu, Edirisuriya MD Siriwardane, Yuqi Song, Alireza Nasiri, and Jianjun Hu · 2021
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Equivariant diffusion for molecule generation in 3D
Emiel Hoogeboom, Víctor Garcia Satorras, Clément Vignac, and Max Welling · 2022
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Illuminating protein space with a programmable generative model
John Ingraham, Max Baranov, Zak Costello, Vincent Frappier, Ahmed Ismail, Shan Tie, Wujie Wang, Vincent Xue, Fritz Obermeyer, Andrew Beam, and Gevorg Grigoryan · 2022
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Yashaswi Pathak, Karandeep Singh Juneja, Girish Varma, Masahiro Ehara, and U Deva Priyakumar · 2020
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Airss data for carbon at 10gpa and the c+n+h+o system at 1gpa, 2020
Chris J. Pickard · 2020
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GraphAF: a flow-based autoregressive model for molecular graph generation
Chence Shi*, Minkai Xu*, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang · 2020
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A generative model for molecular distance geometry
Gregor Simm and Jose Miguel Hernandez-Lobato · 2020
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Atomistic line graph neural network for improved materials property predictions
Kamal Choudhary and Brian DeCost · 2021
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On density estimation with diffusion models
Diederik P Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
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DiffWave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2021
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Torsional diffusion for molecular conformer generation
Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, and Tommi S. Jaakkola · 2022
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An autoregressive flow model for 3D molecular geometry generation from scratch
Youzhi Luo and Shuiwang Ji · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Raphael Gontijo-Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David J. Fleet, and Mohammad Norouzi · 2022
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Deep generative model for periodic graphs
Shiyu Wang, Xiaojie Guo, and Liang Zhao · 2022
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Diffusion-based molecule generation with informative prior bridges
Lemeng Wu, Chengyue Gong, Xingchao Liu, Mao Ye, and qiang liu · 2022
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Crystal diffusion variational autoencoder for periodic material generation
Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, and Tommi S. Jaakkola · 2022
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GeoDiff: A geometric diffusion model for molecular conformation generation
Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang · 2022
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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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Crystal structure prediction by joint equivariant diffusion on lattices and fractional coordinates
Rui Jiao, Wenbing Huang, Peijia Lin, Jiaqi Han, Pin Chen, Yutong Lu, and Yang Liu · 2023
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Efficient approximations of complete interatomic potentials for crystal property prediction
Yuchao Lin, Keqiang Yan, Youzhi Luo, Yi Liu, Xiaoning Qian, and Shuiwang Ji · 2023
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De novo design of protein structure and function with rfdiffusion
Joseph L Watson, David Juergens, Nathaniel R Bennett, Brian L Trippe, Jason Yim, Helen E Eisenach, Woody Ahern, Andrew J Borst, Robert J Ragotte, Lukas F Milles, et al · 2023
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