Prediction of thermal boundary resistance by the machine learning method

Tianzhuo Zhan, Lei Fang, Yibin Xu*

*この研究の対応する著者

研究成果: Article査読

54 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号7109
ジャーナルScientific reports
7
1
DOI
出版ステータスPublished - 2017 12月 1
外部発表はい

ASJC Scopus subject areas

  • 一般

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