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Descriptor selection for predicting interfacial thermal resistance by machine learning methods.

Xiaojuan Tian1, Mingguang Chen2

  • 1Department of Chemical Engineering, China University of Petroleum, Beijing, 102249, China. tian@cup.edu.cn.

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|January 13, 2021
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Machine learning accurately predicts interfacial thermal resistance (ITR) by identifying key material descriptors. This enables faster, reliable material selection for thermal management in nanostructured devices.

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Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Computational Science

Background:

  • Interfacial thermal resistance (ITR) is crucial for nanostructured devices.
  • Predicting ITR is essential for effective thermal management and material selection.

Purpose of the Study:

  • To develop accurate and efficient machine learning models for ITR prediction.
  • To identify key material descriptors that govern ITR.
  • To provide guidelines for material selection in thermal management.

Main Methods:

  • Employed state-of-the-art machine learning techniques.
  • Utilized decision tree (DT) for descriptor importance analysis.
  • Applied univariate selection (UV) for descriptor sorting.
  • Evaluated models using kernel ridge regression, Gaussian process regression, and K-nearest neighbors.

Main Results:

  • Identified optimal descriptor subsets (e.g., top 5 common descriptors from DT and UV).
  • Developed concise machine learning models with performance comparable to models using all descriptors.
  • Demonstrated high accuracy and reliability of the descriptor selection methods.

Conclusions:

  • The proposed descriptor selection strategy yields accurate and reliable ITR prediction models.
  • Concise models enable fast ITR prediction for thermal management applications.
  • This approach facilitates efficient material selection for nanostructured devices.