Identifying superionic conductors by materials informatics and high-throughput synthesis

High-throughput prediction and synthesis are vital for obtaining new materials that deviate from existing compositions. Here, machine learning is combined with high-throughput synthesis to identify superionic conductors based on Ca-(Nb,Ta)-Bi-O.

Guardado en:

Detalles Bibliográficos
Autores principales:	Masato Matsubara, Akitoshi Suzumura, Nobuko Ohba, Ryoji Asahi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492
Acceso en línea:	https://doaj.org/article/37c9bea1495d4ca7b93080fed99149bd
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:37c9bea1495d4ca7b93080fed99149bd
record_format	dspace
spelling	oai:doaj.org-article:37c9bea1495d4ca7b93080fed99149bd2021-12-02T14:06:16ZIdentifying superionic conductors by materials informatics and high-throughput synthesis10.1038/s43246-019-0004-72662-4443https://doaj.org/article/37c9bea1495d4ca7b93080fed99149bd2020-02-01T00:00:00Zhttps://doi.org/10.1038/s43246-019-0004-7https://doaj.org/toc/2662-4443High-throughput prediction and synthesis are vital for obtaining new materials that deviate from existing compositions. Here, machine learning is combined with high-throughput synthesis to identify superionic conductors based on Ca-(Nb,Ta)-Bi-O.Masato MatsubaraAkitoshi SuzumuraNobuko OhbaRyoji AsahiNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492ENCommunications Materials, Vol 1, Iss 1, Pp 1-6 (2020)
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 Masato Matsubara Akitoshi Suzumura Nobuko Ohba Ryoji Asahi Identifying superionic conductors by materials informatics and high-throughput synthesis
description	High-throughput prediction and synthesis are vital for obtaining new materials that deviate from existing compositions. Here, machine learning is combined with high-throughput synthesis to identify superionic conductors based on Ca-(Nb,Ta)-Bi-O.
format	article
author	Masato Matsubara Akitoshi Suzumura Nobuko Ohba Ryoji Asahi
author_facet	Masato Matsubara Akitoshi Suzumura Nobuko Ohba Ryoji Asahi
author_sort	Masato Matsubara
title	Identifying superionic conductors by materials informatics and high-throughput synthesis
title_short	Identifying superionic conductors by materials informatics and high-throughput synthesis
title_full	Identifying superionic conductors by materials informatics and high-throughput synthesis
title_fullStr	Identifying superionic conductors by materials informatics and high-throughput synthesis
title_full_unstemmed	Identifying superionic conductors by materials informatics and high-throughput synthesis
title_sort	identifying superionic conductors by materials informatics and high-throughput synthesis
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/37c9bea1495d4ca7b93080fed99149bd
work_keys_str_mv	AT masatomatsubara identifyingsuperionicconductorsbymaterialsinformaticsandhighthroughputsynthesis AT akitoshisuzumura identifyingsuperionicconductorsbymaterialsinformaticsandhighthroughputsynthesis AT nobukoohba identifyingsuperionicconductorsbymaterialsinformaticsandhighthroughputsynthesis AT ryojiasahi identifyingsuperionicconductorsbymaterialsinformaticsandhighthroughputsynthesis
_version_	1718392012439289856

Identifying superionic conductors by materials informatics and high-throughput synthesis

Ejemplares similares