  With the advances in materials and integration of electronics and
thermoelectrics, the demand for novel crystalline materials with ultimate
high/low thermal conductivity is increasing. However, search for optimal
thermal materials is challenge due to the tremendous degrees of freedom in the
composition and structure of crystal compounds and nanostructures, and thus
empirical search would be exhausting. Materials informatics, which combines the
simulation/experiment with machine learning, is now gaining great attention as
a tool to accelerate the search of novel thermal materials. In this review, we
discuss recent progress in developing materials informatics for heat transport:
the exploration of crystals with high/low thermal conductivity via
high-throughput screening, and nanostructure design for high/low thermal
conductance using the Bayesian optimization and Monte Carlo tree search. The
progresses show that the materials informatics method are useful for designing
thermal functional materials. We end by addressing the remaining issues and
challenges for further development.

Applied Physics;Materials Science