Journal Article

Predicting interfacial thermal resistance by machine learning

Fang, Lei, Yen-Ju, Wu, Xu, Yibin

2019

Overview

Various factors affect the interfacial thermal resistance (ITR) between two materials, making ITR prediction a high-dimensional mathematical problem. Machine learning is a cost-effective method to address this. Here, we report ITR predictive models based on experimental data. The physical, chemical, and material properties of ITR are categorized into three sets of descriptors, and three algorithms are used for the models. Those descriptors assist the models in reducing the mismatch between predicted and experimental values and reaching high predictive performance of 96%. Over 80,000 material systems composed of 293 materials were inputs for predictions. Among the top-100 high-ITR predictions by the three different algorithms, 25 material systems are repeatedly predicted by at least two algorithms. One of the 25 material systems, Bi/Si achieved the ultra-low thermal conductivity in our previous work. We believe that the predicted high-ITR material systems are potential candidates for thermoelectric applications. This study proposed a strategy for material exploration for thermal management by means of machine learning.

• Publisher: Nature Publishing Group
• Subject: Algorithms / Artificial intelligence / Exploration / Heat transfer / Learning algorithms / Machine learning / Material properties / Mathematical models / Organic chemistry / Performance prediction / Prediction models / Thermal conductivity / Thermal energy / Thermal management / Thermal resistance