Predicting interfacial thermal resistance by machine learning

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,...

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Bibliographic Details
Published in:npj computational materials 2019-05, Vol.5 (1), Article 56
Main Authors: Wu, Yen-Ju, Fang, Lei, Xu, Yibin
Format: Article
Language:English
Subjects:
639/301/357
639/925/357
Algorithms
Artificial intelligence
Characterization and Evaluation of Materials
Chemistry and Materials Science
Computational Intelligence
Exploration
Heat transfer
Learning algorithms
Machine learning
Material properties
Materials Science
Mathematical and Computational Engineering
Mathematical and Computational Physics
Mathematical Modeling and Industrial Mathematics
Mathematical models
Organic chemistry
Performance prediction
Prediction models
Theoretical
Thermal conductivity
Thermal energy
Thermal management
Thermal resistance
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Summary: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.
ISSN:2057-3960
2057-3960
DOI:10.1038/s41524-019-0193-0