Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning

Abstract Understanding and controlling the self-assembly of vertically oriented carbon nanotube (CNT) forests is essential for realizing their potential in myriad applications. The governing process–structure–property mechanisms are poorly understood, and the processing parameter space is far too va...

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Autores principales:	Taher Hajilounezhad, Rina Bao, Kannappan Palaniappan, Filiz Bunyak, Prasad Calyam, Matthew R. Maschmann
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/7cbc035ed72342a18a4f677e7c46a9ae
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spelling	oai:doaj.org-article:7cbc035ed72342a18a4f677e7c46a9ae2021-12-02T17:08:43ZPredicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning10.1038/s41524-021-00603-82057-3960https://doaj.org/article/7cbc035ed72342a18a4f677e7c46a9ae2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00603-8https://doaj.org/toc/2057-3960Abstract Understanding and controlling the self-assembly of vertically oriented carbon nanotube (CNT) forests is essential for realizing their potential in myriad applications. The governing process–structure–property mechanisms are poorly understood, and the processing parameter space is far too vast to exhaustively explore experimentally. We overcome these limitations by using a physics-based simulation as a high-throughput virtual laboratory and image-based machine learning to relate CNT forest synthesis attributes to their mechanical performance. Using CNTNet, our image-based deep learning classifier module trained with synthetic imagery, combinations of CNT diameter, density, and population growth rate classes were labeled with an accuracy of >91%. The CNTNet regression module predicted CNT forest stiffness and buckling load properties with a lower root-mean-square error than that of a regression predictor based on CNT physical parameters. These results demonstrate that image-based machine learning trained using only simulated imagery can distinguish subtle CNT forest morphological features to predict physical material properties with high accuracy. CNTNet paves the way to incorporate scanning electron microscope imagery for high-throughput material discovery.Taher HajilounezhadRina BaoKannappan PalaniappanFiliz BunyakPrasad CalyamMatthew R. MaschmannNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Taher Hajilounezhad Rina Bao Kannappan Palaniappan Filiz Bunyak Prasad Calyam Matthew R. Maschmann Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning
description	Abstract Understanding and controlling the self-assembly of vertically oriented carbon nanotube (CNT) forests is essential for realizing their potential in myriad applications. The governing process–structure–property mechanisms are poorly understood, and the processing parameter space is far too vast to exhaustively explore experimentally. We overcome these limitations by using a physics-based simulation as a high-throughput virtual laboratory and image-based machine learning to relate CNT forest synthesis attributes to their mechanical performance. Using CNTNet, our image-based deep learning classifier module trained with synthetic imagery, combinations of CNT diameter, density, and population growth rate classes were labeled with an accuracy of >91%. The CNTNet regression module predicted CNT forest stiffness and buckling load properties with a lower root-mean-square error than that of a regression predictor based on CNT physical parameters. These results demonstrate that image-based machine learning trained using only simulated imagery can distinguish subtle CNT forest morphological features to predict physical material properties with high accuracy. CNTNet paves the way to incorporate scanning electron microscope imagery for high-throughput material discovery.
format	article
author	Taher Hajilounezhad Rina Bao Kannappan Palaniappan Filiz Bunyak Prasad Calyam Matthew R. Maschmann
author_facet	Taher Hajilounezhad Rina Bao Kannappan Palaniappan Filiz Bunyak Prasad Calyam Matthew R. Maschmann
author_sort	Taher Hajilounezhad
title	Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning
title_short	Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning
title_full	Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning
title_fullStr	Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning
title_full_unstemmed	Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning
title_sort	predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7cbc035ed72342a18a4f677e7c46a9ae
work_keys_str_mv	AT taherhajilounezhad predictingcarbonnanotubeforestattributesandmechanicalpropertiesusingsimulatedimagesanddeeplearning AT rinabao predictingcarbonnanotubeforestattributesandmechanicalpropertiesusingsimulatedimagesanddeeplearning AT kannappanpalaniappan predictingcarbonnanotubeforestattributesandmechanicalpropertiesusingsimulatedimagesanddeeplearning AT filizbunyak predictingcarbonnanotubeforestattributesandmechanicalpropertiesusingsimulatedimagesanddeeplearning AT prasadcalyam predictingcarbonnanotubeforestattributesandmechanicalpropertiesusingsimulatedimagesanddeeplearning AT matthewrmaschmann predictingcarbonnanotubeforestattributesandmechanicalpropertiesusingsimulatedimagesanddeeplearning
_version_	1718381506714402816

Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning

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