Alhadeethi, Yas m=Mohammad
KAUST DepartmentPhysical Science and Engineering (PSE) Division
Material Science and Engineering
Material Science and Engineering Program
KAUST Grant NumberCRF-2018-3717-CRG7
Permanent link to this recordhttp://hdl.handle.net/10754/670304
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AbstractInterfacial 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 27 and engineers searching for high melting point, high ITR material systems.
CitationChen, M., Li, J., Tian, B., Al-Hadeethi, Y. M., Arkook, B., Tian, X., & Zhang, X. (2021). Predicting Interfacial Thermal Resistance by Ensemble Learning. Computation, 9(8), 87. doi:10.3390/computation9080087
SponsorsThe work reported was funded by the King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR), under the Award Nos. CRF-2018-3717-CRG7 and CRF-2015-2996 -CRG5, and by the National Natural Science Foundation of China under Award No. 21808240.
PublisherAccepted by MDPI
JournalAccepted by Computation
Except where otherwise noted, this item's license is described as Copyright: © 2021 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license