Machine Learning Chemical Guidelines for Engineering Electronic Structures in Half-Heusler Thermoelectric Materials

Dylla, Maxwell T.; Dunn, Alexander; Anand, Shashwat; Jain, Anubhav; Snyder, G. Jeffrey

doi:10.34133/2020/6375171

Machine Learning Chemical Guidelines for Engineering Electronic Structures in Half-Heusler Thermoelectric Materials

Journal Article · Wed Apr 22 00:00:00 EDT 2020 · Research

DOI:https://doi.org/10.34133/2020/6375171· OSTI ID:1909276

Dylla, Maxwell T. ^[1]; ^[2]; ^[1]; ^[3]; ^[1]

Department of Materials Science and Engineering, Northwestern University, IL 60208, USA
Department of Materials Science and Engineering, UC Berkeley, CA 94720, USA, Lawrence Berkeley National Laboratory, Energy Technologies Area, CA 94720, USA
Lawrence Berkeley National Laboratory, Energy Technologies Area, CA 94720, USA

Half-Heusler materials are strong candidates for thermoelectric applications due to their high weighted mobilities and power factors, which is known to be correlated to valley degeneracy in the electronic band structure. However, there are over 50 known semiconducting half-Heusler phases, and it is not clear how the chemical composition affects the electronic structure. While all the n-type electronic structures have their conduction band minimum at either the $Γ$ - or $X$ -point, there is more diversity in the p-type electronic structures, and the valence band maximum can be at either the $Γ$ -, $L$ -, or $W$ -point. Here, we use high throughput computation and machine learning to compare the valence bands of known half-Heusler compounds and discover new chemical guidelines for promoting the highly degenerate $W$ -point to the valence band maximum. We do this by constructing an “orbital phase diagram” to cluster the variety of electronic structures expressed by these phases into groups, based on the atomic orbitals that contribute most to their valence bands. Then, with the aid of machine learning, we develop new chemical rules that predict the location of the valence band maximum in each of the phases. These rules can be used to engineer band structures with band convergence and high valley degeneracy.

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Research Organization:: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)

Sponsoring Organization:: National Science Foundation (NSF); USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC02-05CH11231; AC02-76SF00515

OSTI ID:: 1909276

Alternate ID(s):: OSTI ID: 1815592

Journal Information:: Research, Journal Name: Research Vol. 2020; ISSN 2639-5274

Publisher:: American Association for the Advancement of Science (AAAS)Copyright Statement

Country of Publication:: United States

Language:: English

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