Finding unprecedentedly low-thermal-conductivity half-Heusler semiconductors via high-throughput materials modeling

Abstract

The lattice thermal conductivity (κω) is a key property for many potential applications of compounds. Discovery of materials with very low or high κω remains an experimental challenge due to high costs and time-consuming synthesis procedures. High-throughput computational pre-screening is a valuable approach for significantly reducing the set of candidate compounds. In this article, we introduce efficient methods for reliably estimating the bulk κω for a large number of compounds. The algorithms are based on a combination of machine-learning algorithms, physical insights, and automatic ab-initio calculations. We scanned approximately 79,000 half-Heusler entries in the AFLOWLIB.org database. Among the 450 mechanically stable ordered semiconductors identified, we find that κω spans more than two orders of magnitude- a much larger range than that previously thought. κω is lowest for compounds whose elements in equivalent positions have large atomic radii. We then perform a thorough screening of thermodynamical stability that allows to reduce the list to 77 systems. We can then provide a quantitative estimate of κω for this selected range of systems. Three semiconductors having κω < 5 W /(m K) are proposed for further experimental study.