Materials Informatics for Heat Transfer: Recent Progresses and Perspectives

• Published in: Nanoscale and microscale thermophysical engineering Vol. 23; no. 2; pp. 157 - 172
• Main Authors: Ju, Shenghong, Shiomi, Junichiro
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 03.04.2019; Taylor & Francis Ltd
• Subjects: Bayesian analysis; Bayesian optimization; Computer simulation; Conductance; Crystal structure; Crystals; Exhausting; Functional materials; Heat conductivity; Heat transfer; Heat transport; high-throughput screening; Informatics; Machine learning; materials informatics; Monte Carlo simulation; Monte Carlo tree search; Nanostructure; nanostructure designing; Optimization; Searching; Thermal conductivity
• ISSN: 1556-7265; 1556-7273
• DOI: 10.1080/15567265.2019.1576816

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 a 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 (MI) 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 MI methods are useful for designing thermal functional materials. We end by addressing the remaining issues and challenges for further development.