Physical and chemical descriptors for predicting interfacial thermal resistance

Area of Science:

• Materials Science
• Condensed Matter Physics
• Thermodynamics

Background:

• Interfacial thermal resistance (ITR) significantly impacts device performance and energy consumption.
• High ITR can be beneficial for thermoelectric materials, enabling ultra-low thermal conductivity through nanostructuring.
• Predicting ITR is crucial for designing advanced materials and devices.

Purpose of the Study:

• To develop a comprehensive dataset of descriptors for predicting interfacial thermal resistance (ITR).
• To provide a resource for understanding and predicting heat transfer phenomena at material interfaces.
• To facilitate the design of materials with tailored thermal properties.

Main Methods:

• Compiled ITR data from 87 experimental papers, covering over 1300 data points for various interface types (metal/nonmetal, nonmetal/nonmetal, metal/metal).
• Created a descriptor dataset for 289 materials, including physical and chemical properties correlated with ITR.
• Included material synthesis methods and thermal measurement techniques for enhanced analysis.

Main Results:

• The dataset enables the construction of over 80,000 pair-material systems for ITR prediction.
• The descriptors identified are highly correlated with interfacial thermal resistance.
• The dataset provides a robust foundation for machine learning-based ITR prediction models.

Conclusions:

• The proposed dataset is a valuable resource for predicting ITR across diverse material systems.
• The data can be utilized for predicting other thermal properties and understanding heat transfer.
• This work advances the field of materials informatics for thermal management applications.