Integrating multiple materials science projects in a single neural network

Traditionally, machine learning for materials science is based on database-specific models and is limited in the number of predictable parameters. Here, a versatile graph-based neural network can integrate multiple data sources, allowing the prediction of more than 40 parameters simultaneously.

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Autores principales:	Kan Hatakeyama-Sato, Kenichi Oyaizu
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/df9217b218ca4e14b7b2a461db83f7d6
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spelling	oai:doaj.org-article:df9217b218ca4e14b7b2a461db83f7d62021-12-02T16:30:09ZIntegrating multiple materials science projects in a single neural network10.1038/s43246-020-00052-82662-4443https://doaj.org/article/df9217b218ca4e14b7b2a461db83f7d62020-07-01T00:00:00Zhttps://doi.org/10.1038/s43246-020-00052-8https://doaj.org/toc/2662-4443Traditionally, machine learning for materials science is based on database-specific models and is limited in the number of predictable parameters. Here, a versatile graph-based neural network can integrate multiple data sources, allowing the prediction of more than 40 parameters simultaneously.Kan Hatakeyama-SatoKenichi OyaizuNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492ENCommunications Materials, Vol 1, Iss 1, Pp 1-10 (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 Kan Hatakeyama-Sato Kenichi Oyaizu Integrating multiple materials science projects in a single neural network
description	Traditionally, machine learning for materials science is based on database-specific models and is limited in the number of predictable parameters. Here, a versatile graph-based neural network can integrate multiple data sources, allowing the prediction of more than 40 parameters simultaneously.
format	article
author	Kan Hatakeyama-Sato Kenichi Oyaizu
author_facet	Kan Hatakeyama-Sato Kenichi Oyaizu
author_sort	Kan Hatakeyama-Sato
title	Integrating multiple materials science projects in a single neural network
title_short	Integrating multiple materials science projects in a single neural network
title_full	Integrating multiple materials science projects in a single neural network
title_fullStr	Integrating multiple materials science projects in a single neural network
title_full_unstemmed	Integrating multiple materials science projects in a single neural network
title_sort	integrating multiple materials science projects in a single neural network
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/df9217b218ca4e14b7b2a461db83f7d6
work_keys_str_mv	AT kanhatakeyamasato integratingmultiplematerialsscienceprojectsinasingleneuralnetwork AT kenichioyaizu integratingmultiplematerialsscienceprojectsinasingleneuralnetwork
_version_	1718383964514680832

Integrating multiple materials science projects in a single neural network

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