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The lattice thermal conductivity ({\kappa}{\omega}) is a key property formany potential applications of compounds. Discovery of materials with very lowor high {\kappa}{\omega} remains an experimental challenge due to high costsand time-consuming synthesis procedures. High-throughput computationalpre-screening is a valuable approach for significantly reducing the set ofcandidate compounds. In this article, we introduce efficient methods forreliably estimating the bulk {\kappa}{\omega} 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 scannedapproximately 79,000 half-Heusler entries in the AFLOWLIB.org database. Amongthe 450 mechanically stable ordered semiconductors identified, we find that{\kappa}{\omega} spans more than two orders of magnitude- a much larger rangethan that previously thought. {\kappa}{\omega} is lowest for compounds whoseelements in equivalent positions have large atomic radii. We then perform athorough screening of thermodynamical stability that allows to reduce the listto 77 systems. We can then provide a quantitative estimate of {\kappa}{\omega}for this selected range of systems. Three semiconductors having{\kappa}{\omega} < 5 W /(m K) are proposed for further experimental study.

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