Prediction of thermal boundary resistance by the machine learning method

Abstract Thermal boundary resistance (TBR) is a key property for the thermal management of high power micro- and opto-electronic devices and for the development of high efficiency thermal barrier coatings and thermoelectric materials. Prediction of TBR is important for guiding the discovery of inter...

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Autores principales:	Tianzhuo Zhan, Lei Fang, Yibin Xu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/813706a4ef82407a80522e53468a3391
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spelling	oai:doaj.org-article:813706a4ef82407a80522e53468a33912021-12-02T12:32:33ZPrediction of thermal boundary resistance by the machine learning method10.1038/s41598-017-07150-72045-2322https://doaj.org/article/813706a4ef82407a80522e53468a33912017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-07150-7https://doaj.org/toc/2045-2322Abstract Thermal boundary resistance (TBR) is a key property for the thermal management of high power micro- and opto-electronic devices and for the development of high efficiency thermal barrier coatings and thermoelectric materials. Prediction of TBR is important for guiding the discovery of interfaces with very low or very high TBR. In this study, we report the prediction of TBR by the machine learning method. We trained machine learning models using the collected experimental TBR data as training data and materials properties that might affect TBR as descriptors. We found that the machine learning models have much better predictive accuracy than the commonly used acoustic mismatch model and diffuse mismatch model. Among the trained models, the Gaussian process regression and the support vector regression models have better predictive accuracy. Also, by comparing the prediction results using different descriptor sets, we found that the film thickness is an important descriptor in the prediction of TBR. These results indicate that machine learning is an accurate and cost-effective method for the prediction of TBR.Tianzhuo ZhanLei FangYibin XuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-9 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tianzhuo Zhan Lei Fang Yibin Xu Prediction of thermal boundary resistance by the machine learning method
description	Abstract Thermal boundary resistance (TBR) is a key property for the thermal management of high power micro- and opto-electronic devices and for the development of high efficiency thermal barrier coatings and thermoelectric materials. Prediction of TBR is important for guiding the discovery of interfaces with very low or very high TBR. In this study, we report the prediction of TBR by the machine learning method. We trained machine learning models using the collected experimental TBR data as training data and materials properties that might affect TBR as descriptors. We found that the machine learning models have much better predictive accuracy than the commonly used acoustic mismatch model and diffuse mismatch model. Among the trained models, the Gaussian process regression and the support vector regression models have better predictive accuracy. Also, by comparing the prediction results using different descriptor sets, we found that the film thickness is an important descriptor in the prediction of TBR. These results indicate that machine learning is an accurate and cost-effective method for the prediction of TBR.
format	article
author	Tianzhuo Zhan Lei Fang Yibin Xu
author_facet	Tianzhuo Zhan Lei Fang Yibin Xu
author_sort	Tianzhuo Zhan
title	Prediction of thermal boundary resistance by the machine learning method
title_short	Prediction of thermal boundary resistance by the machine learning method
title_full	Prediction of thermal boundary resistance by the machine learning method
title_fullStr	Prediction of thermal boundary resistance by the machine learning method
title_full_unstemmed	Prediction of thermal boundary resistance by the machine learning method
title_sort	prediction of thermal boundary resistance by the machine learning method
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/813706a4ef82407a80522e53468a3391
work_keys_str_mv	AT tianzhuozhan predictionofthermalboundaryresistancebythemachinelearningmethod AT leifang predictionofthermalboundaryresistancebythemachinelearningmethod AT yibinxu predictionofthermalboundaryresistancebythemachinelearningmethod
_version_	1718394044333162496

Prediction of thermal boundary resistance by the machine learning method

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