A geometric-information-enhanced crystal graph network for predicting properties of materials

Graph neural networks are an accurate machine learning-based approach for property prediction. Here, a geometric-information-enhanced crystal graph neural network is demonstrated, which accurately predicts the formation energy and band gap of crystalline materials.

Guardado en:

Detalles Bibliográficos
Autores principales:	Jiucheng Cheng, Chunkai Zhang, Lifeng Dong
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/7942b7f2b0a14299851df024aa50898d
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:7942b7f2b0a14299851df024aa50898d
record_format	dspace
spelling	oai:doaj.org-article:7942b7f2b0a14299851df024aa50898d2021-12-02T14:58:46ZA geometric-information-enhanced crystal graph network for predicting properties of materials10.1038/s43246-021-00194-32662-4443https://doaj.org/article/7942b7f2b0a14299851df024aa50898d2021-09-01T00:00:00Zhttps://doi.org/10.1038/s43246-021-00194-3https://doaj.org/toc/2662-4443Graph neural networks are an accurate machine learning-based approach for property prediction. Here, a geometric-information-enhanced crystal graph neural network is demonstrated, which accurately predicts the formation energy and band gap of crystalline materials.Jiucheng ChengChunkai ZhangLifeng DongNature 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 Jiucheng Cheng Chunkai Zhang Lifeng Dong A geometric-information-enhanced crystal graph network for predicting properties of materials
description	Graph neural networks are an accurate machine learning-based approach for property prediction. Here, a geometric-information-enhanced crystal graph neural network is demonstrated, which accurately predicts the formation energy and band gap of crystalline materials.
format	article
author	Jiucheng Cheng Chunkai Zhang Lifeng Dong
author_facet	Jiucheng Cheng Chunkai Zhang Lifeng Dong
author_sort	Jiucheng Cheng
title	A geometric-information-enhanced crystal graph network for predicting properties of materials
title_short	A geometric-information-enhanced crystal graph network for predicting properties of materials
title_full	A geometric-information-enhanced crystal graph network for predicting properties of materials
title_fullStr	A geometric-information-enhanced crystal graph network for predicting properties of materials
title_full_unstemmed	A geometric-information-enhanced crystal graph network for predicting properties of materials
title_sort	geometric-information-enhanced crystal graph network for predicting properties of materials
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7942b7f2b0a14299851df024aa50898d
work_keys_str_mv	AT jiuchengcheng ageometricinformationenhancedcrystalgraphnetworkforpredictingpropertiesofmaterials AT chunkaizhang ageometricinformationenhancedcrystalgraphnetworkforpredictingpropertiesofmaterials AT lifengdong ageometricinformationenhancedcrystalgraphnetworkforpredictingpropertiesofmaterials AT jiuchengcheng geometricinformationenhancedcrystalgraphnetworkforpredictingpropertiesofmaterials AT chunkaizhang geometricinformationenhancedcrystalgraphnetworkforpredictingpropertiesofmaterials AT lifengdong geometricinformationenhancedcrystalgraphnetworkforpredictingpropertiesofmaterials
_version_	1718389273992888320

A geometric-information-enhanced crystal graph network for predicting properties of materials

Ejemplares similares