Predicting Interfacial Thermal Resistance by Ensemble Learning
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/9/8/87 |
| _version_ | 1797524254925783040 |
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| author | Mingguang Chen Junzhu Li Bo Tian Yas Mohammed Al-Hadeethi Bassim Arkook Xiaojuan Tian Xixiang Zhang |
| author_facet | Mingguang Chen Junzhu Li Bo Tian Yas Mohammed Al-Hadeethi Bassim Arkook Xiaojuan Tian Xixiang Zhang |
| author_sort | Mingguang Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T08:54:43Z |
| format | Article |
| id | doaj.art-96c2af8609554883953e3a3e44a4013d |
| institution | Directory Open Access Journal |
| issn | 2079-3197 |
| language | English |
| last_indexed | 2024-03-10T08:54:43Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj.art-96c2af8609554883953e3a3e44a4013d2023-11-22T07:15:26ZengMDPI AGComputation2079-31972021-08-01988710.3390/computation9080087Predicting Interfacial Thermal Resistance by Ensemble LearningMingguang Chen0Junzhu Li1Bo Tian2Yas Mohammed Al-Hadeethi3Bassim Arkook4Xiaojuan Tian5Xixiang Zhang6Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi ArabiaPhysical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi ArabiaPhysical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi ArabiaDepartment of Physics, King Abdulaziz University, Jeddah, Makkah 21589, Saudi ArabiaDepartment of Physics, King Abdulaziz University, Jeddah, Makkah 21589, Saudi ArabiaDepartment of Chemical Engineering, China University of Petroleum, Beijing 102249, ChinaPhysical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi ArabiaInterfacial 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.https://www.mdpi.com/2079-3197/9/8/87interfacial thermal resistanceXGBoostKernel Ridge Regressiondeep neural networksensemble learning |
| spellingShingle | Mingguang Chen Junzhu Li Bo Tian Yas Mohammed Al-Hadeethi Bassim Arkook Xiaojuan Tian Xixiang Zhang Predicting Interfacial Thermal Resistance by Ensemble Learning Computation interfacial thermal resistance XGBoost Kernel Ridge Regression deep neural networks ensemble learning |
| title | Predicting Interfacial Thermal Resistance by Ensemble Learning |
| title_full | Predicting Interfacial Thermal Resistance by Ensemble Learning |
| title_fullStr | Predicting Interfacial Thermal Resistance by Ensemble Learning |
| title_full_unstemmed | Predicting Interfacial Thermal Resistance by Ensemble Learning |
| title_short | Predicting Interfacial Thermal Resistance by Ensemble Learning |
| title_sort | predicting interfacial thermal resistance by ensemble learning |
| topic | interfacial thermal resistance XGBoost Kernel Ridge Regression deep neural networks ensemble learning |
| url | https://www.mdpi.com/2079-3197/9/8/87 |
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