Deep neural networks for accurate predictions of crystal stability

Crystal stability prediction is of paramount importance for novel material discovery, with theoretical approaches alternative to expensive standard schemes highly desired. Here the authors develop a deep learning approach which, just using two descriptors, provides crystalline formation energies wit...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Weike Ye, Chi Chen, Zhenbin Wang, Iek-Heng Chu, Shyue Ping Ong
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/4d18a79643814bec9e968a7650b9f51d
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:4d18a79643814bec9e968a7650b9f51d
record_format	dspace
spelling	oai:doaj.org-article:4d18a79643814bec9e968a7650b9f51d2021-12-02T14:41:28ZDeep neural networks for accurate predictions of crystal stability10.1038/s41467-018-06322-x2041-1723https://doaj.org/article/4d18a79643814bec9e968a7650b9f51d2018-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-018-06322-xhttps://doaj.org/toc/2041-1723Crystal stability prediction is of paramount importance for novel material discovery, with theoretical approaches alternative to expensive standard schemes highly desired. Here the authors develop a deep learning approach which, just using two descriptors, provides crystalline formation energies with very high accuracy.Weike YeChi ChenZhenbin WangIek-Heng ChuShyue Ping OngNature PortfolioarticleScienceQENNature Communications, Vol 9, Iss 1, Pp 1-6 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Weike Ye Chi Chen Zhenbin Wang Iek-Heng Chu Shyue Ping Ong Deep neural networks for accurate predictions of crystal stability
description	Crystal stability prediction is of paramount importance for novel material discovery, with theoretical approaches alternative to expensive standard schemes highly desired. Here the authors develop a deep learning approach which, just using two descriptors, provides crystalline formation energies with very high accuracy.
format	article
author	Weike Ye Chi Chen Zhenbin Wang Iek-Heng Chu Shyue Ping Ong
author_facet	Weike Ye Chi Chen Zhenbin Wang Iek-Heng Chu Shyue Ping Ong
author_sort	Weike Ye
title	Deep neural networks for accurate predictions of crystal stability
title_short	Deep neural networks for accurate predictions of crystal stability
title_full	Deep neural networks for accurate predictions of crystal stability
title_fullStr	Deep neural networks for accurate predictions of crystal stability
title_full_unstemmed	Deep neural networks for accurate predictions of crystal stability
title_sort	deep neural networks for accurate predictions of crystal stability
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/4d18a79643814bec9e968a7650b9f51d
work_keys_str_mv	AT weikeye deepneuralnetworksforaccuratepredictionsofcrystalstability AT chichen deepneuralnetworksforaccuratepredictionsofcrystalstability AT zhenbinwang deepneuralnetworksforaccuratepredictionsofcrystalstability AT iekhengchu deepneuralnetworksforaccuratepredictionsofcrystalstability AT shyuepingong deepneuralnetworksforaccuratepredictionsofcrystalstability
_version_	1718389868924502016

Deep neural networks for accurate predictions of crystal stability

Ejemplares similares