Abstract
Interfacial thermal resistance (ITR) plays a critical role in the thermal properties of a variety of material systems. Accurate and reliable ITR prediction is vital in the structure design and thermal management of nanodevices, aircraft, buildings, etc. However, because ITR is affected by dozens of factors, traditional models have difficulty predicting it. To address this high-dimensional problem, we employ machine learning and deep learning algorithms in this work. First, exploratory data analysis and data visualization were performed on the raw data to obtain a comprehensive picture of the objects. Second, XGBoost was chosen to demonstrate the significance of various descriptors in ITR prediction. Following that, the top 20 descriptors with the highest importance scores were chosen except for fdensity, fmass, and smass, to build concise models based on XGBoost, Kernel Ridge Regression, and deep neural network algorithms. Finally, ensemble learning was used to combine all three models and predict high melting points, high ITR material systems for spacecraft, automotive, building insulation, etc. The predicted ITR of the Pb/diamond high melting point material system was consistent with the experimental value reported in the literature, while the other predicted material systems provide valuable guidelines for experimentalists and engineers searching for high melting point, high ITR material systems.
Highlights
Interfacial thermal resistance (ITR) is a property that measures an interface’s resistance to thermal flow [1,2,3]
It is important to note that descriptor selection should not be based solely on the absolute value of descriptor correlations with ITR
For ITR prediction, an ensemble model based on the XGB, KRR, and deep neural network (DNN) algorithms was developed
Summary
Interfacial thermal resistance (ITR) is a property that measures an interface’s resistance to thermal flow [1,2,3]. When thermal flux is applied across an interface, ITR causes a finite temperature discontinuity. The growing interest in space exploration necessitates developing special material systems that can withstand high temperatures and have high ITR. A reliable and accurate prediction of ITR is critical for the design of materials with desired properties. ITR is affected by a wide range of factors, including melting point, film thickness, material density, heat capacity, electronegativity, binding energy, and temperature [7,8]. ITR prediction is a high-dimensional problem that cannot be solved ideally using traditional methods
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