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Crystal Structure Prediction (CSP) is crucial in various scientific disciplines.
Self-consistent equations including exchange and correlation effects
Walter Kohn and Lu Jeu Sham · 1965
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Projector augmented-wave method
Peter E Blöchl · 1994
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Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set
Georg Kresse and Jürgen Furthmüller · 1996
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Generalized gradient approximation made simple
John P Perdew, Kieron Burke, and Matthias Ernzerhof · 1996
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Cryptic crystallography
Gautam R Desiraju · 2002
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Crystal structure prediction by data mining
Detlef WM Hofmann and Joannis Apostolakis · 2003
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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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Uspex—evolutionary crystal structure prediction
Colin W Glass, Artem R Oganov, and Nikolaus Hansen · 2006
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Crystal structure prediction via particle-swarm optimization
Yanchao Wang, Jian Lv, Li Zhu, and Yanming Ma · 2010
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Ab initio random structure searching
Chris J Pickard and RJ Needs · 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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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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Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
James Bergstra, Daniel Yamins, and David Cox · 2013
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Efficient evaluation of the probability density function of a wrapped normal distribution
Gerhard Kurz, Igor Gilitschenski, and Uwe D Hanebeck · 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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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 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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Computer-assisted inverse design of inorganic electrides
Yunwei Zhang, Hui Wang, Yanchao Wang, Lijun Zhang, and Yanming Ma · 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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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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Crystal structure prediction accelerated by bayesian optimization
Tomoki Yamashita, Nobuya Sato, Hiori Kino, Takashi Miyake, Koji Tsuda, and Tamio Oguchi · 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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On-the-fly machine learning of atomic potential in density functional theory structure optimization
TL Jacobsen, MS Jørgensen, and B Hammer · 2018
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Crystalgan: learning to discover crystallographic structures with generative adversarial networks
Asma Nouira, Nataliya Sokolovska, and Jean-Claude Crivello · 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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Structure prediction drives materials discovery
Artem R Oganov, Chris J Pickard, Qiang Zhu, and Richard J Needs · 2019
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Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
Evgeny V Podryabinkin, Evgeny V Tikhonov, Alexander V Shapeev, and Artem R Oganov · 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
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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An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties
Zekun Ren, Siyu Isaac Parker Tian, Juhwan Noh, Felipe Oviedo, Guangzong Xing, Jiali Li, Qiaohao Liang, Ruiming Zhu, Armin G. Aberle, Shijing Sun, Xiaonan Wang, Yi Liu, Qianxiao Li, Senthilnath Jayavelu, Kedar Hippalgaonkar, Yousung Jung, and Tonio Buonassisi · 2021
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E (n) equivariant graph neural networks
Vıctor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
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Equivariant transformers for neural network based molecular potentials
Philipp Thölke and Gianni De Fabritiis · 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, Aini Palizhati, Anuroop Sriram, Brandon Wood, Junwoong Yoon, Devi Parikh, C. Lawrence Zitnick, and Zachary Ulissi · 2021
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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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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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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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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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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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Distance matrix-based crystal structure prediction using evolutionary algorithms
Jianjun Hu, Wenhui Yang, and Edirisuriya M Dilanga Siriwardane · 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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High-resolution image synthesis with latent diffusion models, 2021
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Improved denoising diffusion probabilistic models
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Gemnet: Universal directional graph neural networks for molecules
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Argmax flows and multinomial diffusion: Learning categorical distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling · 2021
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Torsional diffusion for molecular conformer generation
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Crystal structure prediction by combining graph network and optimization algorithm
Guanjian Cheng, Xin-Gao Gong, and Wan-Jian Yin · 2022
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The open catalyst 2022 (oc22) dataset and challenges for oxide electrocatalysis
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Periodic graph transformers for crystal material property prediction
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Hierarchical text-conditional image generation with clip latents
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Equivariant diffusion for molecule generation in 3d
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Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures
Shitong Luo, Yufeng Su, Xingang Peng, Sheng Wang, Jian Peng, and Jianzhu Ma · 2022
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Riemannian score-based generative modelling
Valentin De Bortoli, Emile Mathieu, Michael John Hutchinson, James Thornton, Yee Whye Teh, and Arnaud Doucet · 2022
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Riemannian diffusion models
Chin-Wei Huang, Milad Aghajohari, Joey Bose, Prakash Panangaden, and Aaron Courville · 2022
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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 · 2022
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EGSDE: Unpaired image-to-image translation via energy-guided stochastic differential equations
Min Zhao, Fan Bao, Chongxuan Li, and Jun Zhu · 2022
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Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem
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