Predicting synthesizability of crystalline materials via deep learning

Predicting the synthesizability of unknown crystals is important for accelerating materials discovery. Here, the synthesizability of crystals with any given composition and structure can be predicted by a deep learning model that maps crystals onto color-coded 3D images processed by convolutional ne...

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Autores principales:	Ali Davariashtiyani, Zahra Kadkhodaie, Sara Kadkhodaei
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492
Acceso en línea:	https://doaj.org/article/286e3c14437f415882db24b057ccb59c
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spelling	oai:doaj.org-article:286e3c14437f415882db24b057ccb59c2021-11-21T12:15:04ZPredicting synthesizability of crystalline materials via deep learning10.1038/s43246-021-00219-x2662-4443https://doaj.org/article/286e3c14437f415882db24b057ccb59c2021-11-01T00:00:00Zhttps://doi.org/10.1038/s43246-021-00219-xhttps://doaj.org/toc/2662-4443Predicting the synthesizability of unknown crystals is important for accelerating materials discovery. Here, the synthesizability of crystals with any given composition and structure can be predicted by a deep learning model that maps crystals onto color-coded 3D images processed by convolutional neural networks.Ali DavariashtiyaniZahra KadkhodaieSara KadkhodaeiNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492ENCommunications Materials, Vol 2, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Ali Davariashtiyani Zahra Kadkhodaie Sara Kadkhodaei Predicting synthesizability of crystalline materials via deep learning
description	Predicting the synthesizability of unknown crystals is important for accelerating materials discovery. Here, the synthesizability of crystals with any given composition and structure can be predicted by a deep learning model that maps crystals onto color-coded 3D images processed by convolutional neural networks.
format	article
author	Ali Davariashtiyani Zahra Kadkhodaie Sara Kadkhodaei
author_facet	Ali Davariashtiyani Zahra Kadkhodaie Sara Kadkhodaei
author_sort	Ali Davariashtiyani
title	Predicting synthesizability of crystalline materials via deep learning
title_short	Predicting synthesizability of crystalline materials via deep learning
title_full	Predicting synthesizability of crystalline materials via deep learning
title_fullStr	Predicting synthesizability of crystalline materials via deep learning
title_full_unstemmed	Predicting synthesizability of crystalline materials via deep learning
title_sort	predicting synthesizability of crystalline materials via deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/286e3c14437f415882db24b057ccb59c
work_keys_str_mv	AT alidavariashtiyani predictingsynthesizabilityofcrystallinematerialsviadeeplearning AT zahrakadkhodaie predictingsynthesizabilityofcrystallinematerialsviadeeplearning AT sarakadkhodaei predictingsynthesizabilityofcrystallinematerialsviadeeplearning
_version_	1718419113480552448

Predicting synthesizability of crystalline materials via deep learning

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