$Deep learning model to predict fracture mechanisms of graphene$
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Deep learning model to predict fracture mechanisms of graphene

Abstract Understanding fracture is critical to the design of resilient nanomaterials. Molecular dynamics offers a way to study fracture at an atomistic level, but is computationally expensive with limitations of scalability. In this work, we build upon machine-learning approaches for predicting nano...

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Autores principales:	Andrew J. Lew, Chi-Hua Yu, Yu-Chuan Hsu, Markus J. Buehler
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/6ca34e2dd038452d92d4550ba477cea5
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spelling	oai:doaj.org-article:6ca34e2dd038452d92d4550ba477cea52021-12-02T13:40:49ZDeep learning model to predict fracture mechanisms of graphene10.1038/s41699-021-00228-x2397-7132https://doaj.org/article/6ca34e2dd038452d92d4550ba477cea52021-04-01T00:00:00Zhttps://doi.org/10.1038/s41699-021-00228-xhttps://doaj.org/toc/2397-7132Abstract Understanding fracture is critical to the design of resilient nanomaterials. Molecular dynamics offers a way to study fracture at an atomistic level, but is computationally expensive with limitations of scalability. In this work, we build upon machine-learning approaches for predicting nanoscopic fracture mechanisms including crack instabilities and branching as a function of crystal orientation. We focus on a particular technologically relevant material system, graphene, and apply a deep learning method to the study of such nanomaterials and explore the parameter space necessary for calibrating machine-learning predictions to meaningful results. Our results validate the ability of deep learning methods to quantitatively capture graphene fracture behavior, including its fractal dimension as a function of crystal orientation, and provide promise toward the wider application of deep learning to materials design, opening the potential for other 2D materials.Andrew J. LewChi-Hua YuYu-Chuan HsuMarkus J. BuehlerNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492ChemistryQD1-999ENnpj 2D Materials and Applications, Vol 5, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Chemistry QD1-999
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Chemistry QD1-999 Andrew J. Lew Chi-Hua Yu Yu-Chuan Hsu Markus J. Buehler Deep learning model to predict fracture mechanisms of graphene
description	Abstract Understanding fracture is critical to the design of resilient nanomaterials. Molecular dynamics offers a way to study fracture at an atomistic level, but is computationally expensive with limitations of scalability. In this work, we build upon machine-learning approaches for predicting nanoscopic fracture mechanisms including crack instabilities and branching as a function of crystal orientation. We focus on a particular technologically relevant material system, graphene, and apply a deep learning method to the study of such nanomaterials and explore the parameter space necessary for calibrating machine-learning predictions to meaningful results. Our results validate the ability of deep learning methods to quantitatively capture graphene fracture behavior, including its fractal dimension as a function of crystal orientation, and provide promise toward the wider application of deep learning to materials design, opening the potential for other 2D materials.
format	article
author	Andrew J. Lew Chi-Hua Yu Yu-Chuan Hsu Markus J. Buehler
author_facet	Andrew J. Lew Chi-Hua Yu Yu-Chuan Hsu Markus J. Buehler
author_sort	Andrew J. Lew
title	Deep learning model to predict fracture mechanisms of graphene
title_short	Deep learning model to predict fracture mechanisms of graphene
title_full	Deep learning model to predict fracture mechanisms of graphene
title_fullStr	Deep learning model to predict fracture mechanisms of graphene
title_full_unstemmed	Deep learning model to predict fracture mechanisms of graphene
title_sort	deep learning model to predict fracture mechanisms of graphene
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6ca34e2dd038452d92d4550ba477cea5
work_keys_str_mv	AT andrewjlew deeplearningmodeltopredictfracturemechanismsofgraphene AT chihuayu deeplearningmodeltopredictfracturemechanismsofgraphene AT yuchuanhsu deeplearningmodeltopredictfracturemechanismsofgraphene AT markusjbuehler deeplearningmodeltopredictfracturemechanismsofgraphene
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Deep learning model to predict fracture mechanisms of graphene

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