Prediction of thermal boundary resistance by the machine learning method

Tianzhuo Zhan, Lei Fang, Yibin Xu

Research output: Contribution to journalArticle

12 Citations (Scopus)

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.

Original languageEnglish
Article number7109
JournalScientific Reports
Volume7
Issue number1
DOIs
Publication statusPublished - 2017 Dec 1
Externally publishedYes

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Learning systems
Thermal barrier coatings
Hot Temperature
Optoelectronic devices
Film thickness
Materials properties
Acoustics
Costs

ASJC Scopus subject areas

  • General

Cite this

Prediction of thermal boundary resistance by the machine learning method. / Zhan, Tianzhuo; Fang, Lei; Xu, Yibin.

In: Scientific Reports, Vol. 7, No. 1, 7109, 01.12.2017.

Research output: Contribution to journalArticle

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