Predicting Interfacial Thermal Resistance by Ensemble Learning

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.

Effects of Thermal Boundary Resistance on Thermal Management of Gallium-Nitride-Based Semiconductor Devices: A Review

“…However, the first-principles calculation method is computationally demanding and time consuming. In recent years, machine learning has emerged as a novel approach for TBR prediction [48][49][50][51][52][53][54][55][56][57], and it has been demonstrated to achieve higher predictive accuracy than the conventionally used AMM and DMM methods [48]. Figure 3b shows the comparison of the correlation between the experimental values and the values predicted by the AMM, DMM, and machine learning method using Gaussian process regression (GPR).…”

Recent advances in designing thermoelectric materials

“…450,451 Prediction models for thermoelectric interfaces in devices have also been explored recently. [452][453][454][455][456] The currently available analytical, highthroughput and machine learning models demonstrate advantages such as reasonable accuracy in prediction for a wide temperature range and several interface types; however, they are mainly limited by the physics of scattering mechanisms in various external conditions, and the availability of experimental or computational data. Therefore, there is room for developing approaches through the underlying theory or considerably accurate prediction by extensive data mining.…”