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Advances in materials informatics (MI), which combines material property calculations/measurements and informatics algorithms, have realized properties in the nanostructures of thermal functional materials beyond what is accessible using empirical approaches based on physical instincts and models. In this Tutorial, we introduce technological procedures and underlying knowledge of MI combining thermal transport calculations and machine learning using an optimization problem of superlattice structures as an example (sample script available in the supplement). To provide fundamental guidance on how to use MI, we describe practical details about descriptors, objective functions, property calculators, machine learning (Bayesian optimization) algorithms, and optimization efficiencies. We then briefly review the recent successful applications of MI to design thermoelectric and thermal radiation materials. Finally, we summarize and provide future perspectives about the topic.

Designing thermal functional materials by coupling thermal transport calculations and machine learning