• Publisher-DOI

Macroscopic thermal properties of engineered or inherent composites depend substantially on the composite structure and the interface characteristics. While it is acknowledged that unveiling such dependency relation is essential for materials design, the complexity involved in, e.g., microstructure representation and limited data impedes the research progress. Herein, this issue is tackled by machine learning techniques on image‐based microstructure and property data predicted from physics simulations, along with experimental validation. The methodology is demonstrated for the model system (Hf₀.₇Ta₀.₃)C/SiC ultrahigh‐temperature ceramic nanocomposite. The structure is reconstructed from scanning electron microscope images, and is resolved by a diffuse‐interface representation, which is advantageous in handling complicated structure and interface properties. Subsequently, hierarchical finite element homogenization is carried out to evaluate the effective thermal conductivity. A thorough comparison between the computed results and experimentally measured data, conducted across diverse temperatures and varying interface thermal resistances, reveals a high level of agreement. The observed agreement allows for the inverse estimation of the interface thermal resistance, a parameter typically challenging to ascertain directly through experimental means. Utilizing comprehensive data, a machine learning surrogate model has been meticulously trained to accurately predict the effective thermal conductivity of composite structures with exceptional performance.

Special Issue: Materials Compounds from Composite Materials for Applications in Extreme Conditions