Finding Unprecedentedly Low-Thermal-Conductivity Half-Heusler Semiconductors via High-Throughput Materials Modeling

By Jesús Carrete, Wu Li, Natalio Mingo, Shidong Wang, Stefano Curtarolo. 2014

363 citations recorded by the source corpus.

The lattice thermal conductivity ({\kappa}{\omega}) is a key property for many potential applications of compounds. Discovery of materials with very low or high {\kappa}{\omega} 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 {\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 scanned approximately 79,000 half-Heusler entries in the AFLOWLIB.org database. Among the 450 mechanically stable ordered semiconductors identified, we find that {\kappa}{\omega} spans more than two orders of magnitude- a much larger range than that previously thought. {\kappa}{\omega} 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 {\kappa}{\omega} for this selected range of systems. Three semiconductors having {\kappa}{\omega} < 5 W /(m K) are proposed for further experimental study.

Subfields: Electronic, Optical and Magnetic Materials, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/93079b743c691e5803788b9fff85590061edc7e5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electronic, Optical and Magnetic Materials

In Materials Chemistry

Citations in the corpus

Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation (2020, background): “On the other hand, researchers have also been seeking insulation materials with extremely lower thermal conductivity [5,6] or materials with lower electronic conductivities, etc.”
On the tuning of electrical and thermal transport in thermoelectrics: an integrated theory–experiment perspective (2016, result): “to evaluate the lattice thermal conductivity of 75 half-Heusler alloys out of 79,057 entries.(190) These results, combined with the electrical transport calculations, result to the ZT evaluations, with about 15 % of these compounds may have ZT42 at high temperatures.”
Survey of ab initio phonon thermal transport (2018, background): “Some work has applied a more Edisonian approach to test PBT/ DFT k calculations by comparing with experimental values for a broad range of materials including zincblende and wurtzite compound III-V [40,45], IIb-VI and 1b-VII [40], and full-Heusler [61] semiconductors or by applying more sophisticate”
Materials informatics (2018, methodology): “The same dataset was then used to search for low-thermal conductivity semiconductors (Carrete et al. 2014) and for novel magnets (Sanvito 2017).”
Predicting the thermodynamic stability of perovskite oxides using machine learning models (2018, methodology): “Recently, high-throughput DFT methods have been used to guide the discovery of new compounds for numerous applications, including: perovskite oxides for solid oxide fuel cell (SOFC) cathodes[1, 2], thermochemical water splitting,[3] half-heusler and sintered compounds for thermoelectrics,[4, 5] oxid”
Reformulation of DFT + U as a pseudohybrid hubbard density functional for accelerated materials discovery (2015, background): “Higher-order levels of theory exist that are able to predict, with great accuracy, the energy gaps (the GW approximation [12] and dynamical mean-field theory [13–15], among others), but they are computationally expensive and unsuitable for extensive high-throughput materials characterization [16,17]”