Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures

Abstract Autonomous materials discovery with desired properties is one of the ultimate goals for materials science, and the current studies have been focusing mostly on high-throughput screening based on density functional theory calculations and forward modeling of physical properties using machine...

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Autores principales:	Teng Long, Nuno M. Fortunato, Ingo Opahle, Yixuan Zhang, Ilias Samathrakis, Chen Shen, Oliver Gutfleisch, Hongbin Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/6d1f4eebd57e4377b41f8f324c970e86
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spelling	oai:doaj.org-article:6d1f4eebd57e4377b41f8f324c970e862021-12-02T14:35:33ZConstrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures10.1038/s41524-021-00526-42057-3960https://doaj.org/article/6d1f4eebd57e4377b41f8f324c970e862021-05-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00526-4https://doaj.org/toc/2057-3960Abstract Autonomous materials discovery with desired properties is one of the ultimate goals for materials science, and the current studies have been focusing mostly on high-throughput screening based on density functional theory calculations and forward modeling of physical properties using machine learning. Applying the deep learning techniques, we have developed a generative model, which can predict distinct stable crystal structures by optimizing the formation energy in the latent space. It is demonstrated that the optimization of physical properties can be integrated into the generative model as on-top screening or backward propagator, both with their own advantages. Applying the generative models on the binary Bi-Se system reveals that distinct crystal structures can be obtained covering the whole composition range, and the phases on the convex hull can be reproduced after the generated structures are fully relaxed to the equilibrium. The method can be extended to multicomponent systems for multi-objective optimization, which paves the way to achieve the inverse design of materials with optimal properties.Teng LongNuno M. FortunatoIngo OpahleYixuan ZhangIlias SamathrakisChen ShenOliver GutfleischHongbin ZhangNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Teng Long Nuno M. Fortunato Ingo Opahle Yixuan Zhang Ilias Samathrakis Chen Shen Oliver Gutfleisch Hongbin Zhang Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures
description	Abstract Autonomous materials discovery with desired properties is one of the ultimate goals for materials science, and the current studies have been focusing mostly on high-throughput screening based on density functional theory calculations and forward modeling of physical properties using machine learning. Applying the deep learning techniques, we have developed a generative model, which can predict distinct stable crystal structures by optimizing the formation energy in the latent space. It is demonstrated that the optimization of physical properties can be integrated into the generative model as on-top screening or backward propagator, both with their own advantages. Applying the generative models on the binary Bi-Se system reveals that distinct crystal structures can be obtained covering the whole composition range, and the phases on the convex hull can be reproduced after the generated structures are fully relaxed to the equilibrium. The method can be extended to multicomponent systems for multi-objective optimization, which paves the way to achieve the inverse design of materials with optimal properties.
format	article
author	Teng Long Nuno M. Fortunato Ingo Opahle Yixuan Zhang Ilias Samathrakis Chen Shen Oliver Gutfleisch Hongbin Zhang
author_facet	Teng Long Nuno M. Fortunato Ingo Opahle Yixuan Zhang Ilias Samathrakis Chen Shen Oliver Gutfleisch Hongbin Zhang
author_sort	Teng Long
title	Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures
title_short	Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures
title_full	Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures
title_fullStr	Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures
title_full_unstemmed	Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures
title_sort	constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6d1f4eebd57e4377b41f8f324c970e86
work_keys_str_mv	AT tenglong constrainedcrystalsdeepconvolutionalgenerativeadversarialnetworkfortheinversedesignofcrystalstructures AT nunomfortunato constrainedcrystalsdeepconvolutionalgenerativeadversarialnetworkfortheinversedesignofcrystalstructures AT ingoopahle constrainedcrystalsdeepconvolutionalgenerativeadversarialnetworkfortheinversedesignofcrystalstructures AT yixuanzhang constrainedcrystalsdeepconvolutionalgenerativeadversarialnetworkfortheinversedesignofcrystalstructures AT iliassamathrakis constrainedcrystalsdeepconvolutionalgenerativeadversarialnetworkfortheinversedesignofcrystalstructures AT chenshen constrainedcrystalsdeepconvolutionalgenerativeadversarialnetworkfortheinversedesignofcrystalstructures AT olivergutfleisch constrainedcrystalsdeepconvolutionalgenerativeadversarialnetworkfortheinversedesignofcrystalstructures AT hongbinzhang constrainedcrystalsdeepconvolutionalgenerativeadversarialnetworkfortheinversedesignofcrystalstructures
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Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures

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