The interfacial thermal resistance (ITR) inside the particulate-filled polymer composite is a bottleneck for improving the thermal conductivity (TC) of the material. Getting full knowledge of the ITR is crucial to the material design as well as to a faithful prediction of TC of the composite. However, a method fully taking into account the local circumstances inside the composite is yet to be developed to precisely characterize the ITR. Here, we propose a comprehensive framework combining high-throughput numerical simulations, machine learning and optimization algorithms, and experiments, which is demonstrated to be robust for the accurate determination of ITRs inside the particulate-filled composites. The strategy extracts as much information as possible about the structure and heat transfer characteristics of the composite based on simple experiments, which lays the foundation for the method to be effective. We show that the polymer-filler ITRs and the effective filler-filler contact ITRs predicted with the method faithfully represent the true characteristics inside the composite materials; they also provide the exact effective parameters, which cannot be obtained from experiments, for accurate numerical prediction of TCs of composite materials with high efficiency. As a result, the framework not only provides a robust tool for accurate characterization of ITRs inside composites but also paves the way for virtual high-throughput formula screening of thermally conductive composite materials that could be used in industrial product design.
Keywords: genetic algorithm; high-throughput computation; interfacial thermal resistance; machine learning; particulate-filled composites.