Accelerated crystal structure prediction of multi-elements random alloy using expandable features

Abstract Properties of solid-state materials depend on their crystal structures. In solid solution high entropy alloy (HEA), its mechanical properties such as strength and ductility depend on its phase. Therefore, the crystal structure prediction should be preceded to find new functional materials....

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Autores principales:	Taewon Jin, Ina Park, Taesu Park, Jaesik Park, Ji Hoon Shim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/781953e0d7574821acda5f2171f01438
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spelling	oai:doaj.org-article:781953e0d7574821acda5f2171f014382021-12-02T13:20:14ZAccelerated crystal structure prediction of multi-elements random alloy using expandable features10.1038/s41598-021-84544-82045-2322https://doaj.org/article/781953e0d7574821acda5f2171f014382021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84544-8https://doaj.org/toc/2045-2322Abstract Properties of solid-state materials depend on their crystal structures. In solid solution high entropy alloy (HEA), its mechanical properties such as strength and ductility depend on its phase. Therefore, the crystal structure prediction should be preceded to find new functional materials. Recently, the machine learning-based approach has been successfully applied to the prediction of structural phases. However, since about 80% of the data set is used as a training set in machine learning, it is well known that it requires vast cost for preparing a dataset of multi-element alloy as training. In this work, we develop an efficient approach to predicting the multi-element alloys' structural phases without preparing a large scale of the training dataset. We demonstrate that our method trained from binary alloy dataset can be applied to the multi-element alloys' crystal structure prediction by designing a transformation module from raw features to expandable form. Surprisingly, without involving the multi-element alloys in the training process, we obtain an accuracy, 80.56% for the phase of the multi-element alloy and 84.20% accuracy for the phase of HEA. It is comparable with the previous machine learning results. Besides, our approach saves at least three orders of magnitude computational cost for HEA by employing expandable features. We suggest that this accelerated approach can be applied to predicting various structural properties of multi-elements alloys that do not exist in the current structural database.Taewon JinIna ParkTaesu ParkJaesik ParkJi Hoon ShimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Taewon Jin Ina Park Taesu Park Jaesik Park Ji Hoon Shim Accelerated crystal structure prediction of multi-elements random alloy using expandable features
description	Abstract Properties of solid-state materials depend on their crystal structures. In solid solution high entropy alloy (HEA), its mechanical properties such as strength and ductility depend on its phase. Therefore, the crystal structure prediction should be preceded to find new functional materials. Recently, the machine learning-based approach has been successfully applied to the prediction of structural phases. However, since about 80% of the data set is used as a training set in machine learning, it is well known that it requires vast cost for preparing a dataset of multi-element alloy as training. In this work, we develop an efficient approach to predicting the multi-element alloys' structural phases without preparing a large scale of the training dataset. We demonstrate that our method trained from binary alloy dataset can be applied to the multi-element alloys' crystal structure prediction by designing a transformation module from raw features to expandable form. Surprisingly, without involving the multi-element alloys in the training process, we obtain an accuracy, 80.56% for the phase of the multi-element alloy and 84.20% accuracy for the phase of HEA. It is comparable with the previous machine learning results. Besides, our approach saves at least three orders of magnitude computational cost for HEA by employing expandable features. We suggest that this accelerated approach can be applied to predicting various structural properties of multi-elements alloys that do not exist in the current structural database.
format	article
author	Taewon Jin Ina Park Taesu Park Jaesik Park Ji Hoon Shim
author_facet	Taewon Jin Ina Park Taesu Park Jaesik Park Ji Hoon Shim
author_sort	Taewon Jin
title	Accelerated crystal structure prediction of multi-elements random alloy using expandable features
title_short	Accelerated crystal structure prediction of multi-elements random alloy using expandable features
title_full	Accelerated crystal structure prediction of multi-elements random alloy using expandable features
title_fullStr	Accelerated crystal structure prediction of multi-elements random alloy using expandable features
title_full_unstemmed	Accelerated crystal structure prediction of multi-elements random alloy using expandable features
title_sort	accelerated crystal structure prediction of multi-elements random alloy using expandable features
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/781953e0d7574821acda5f2171f01438
work_keys_str_mv	AT taewonjin acceleratedcrystalstructurepredictionofmultielementsrandomalloyusingexpandablefeatures AT inapark acceleratedcrystalstructurepredictionofmultielementsrandomalloyusingexpandablefeatures AT taesupark acceleratedcrystalstructurepredictionofmultielementsrandomalloyusingexpandablefeatures AT jaesikpark acceleratedcrystalstructurepredictionofmultielementsrandomalloyusingexpandablefeatures AT jihoonshim acceleratedcrystalstructurepredictionofmultielementsrandomalloyusingexpandablefeatures
_version_	1718393219072393216

Accelerated crystal structure prediction of multi-elements random alloy using expandable features

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