Dylla Maxwell T, Dunn Alexander, Anand Shashwat, Jain Anubhav, Snyder G Jeffrey
Department of Materials Science and Engineering, Northwestern University, IL 60208, USA.
Department of Materials Science and Engineering, UC Berkeley, CA 94720, USA.
Research (Wash D C). 2020 Apr 22;2020:6375171. doi: 10.34133/2020/6375171. eCollection 2020.
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 -point, there is more diversity in the p-type electronic structures, and the valence band maximum can be at either the Γ-, -, or -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 -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.
由于具有高加权迁移率和功率因数，半赫斯勒材料是热电应用的有力候选材料，而这与电子能带结构中的能谷简并性相关。然而，已知的半导体半赫斯勒相有50多种，目前尚不清楚化学成分如何影响电子结构。虽然所有n型电子结构的导带最小值都在Γ点或L点，但p型电子结构的多样性更大，价带最大值可以在Γ点、L点或X点。在此，我们使用高通量计算和机器学习来比较已知半赫斯勒化合物的价带，并发现将高度简并的L点提升到价带最大值的新化学准则。我们通过构建一个“轨道相图”来实现这一点，该相图根据对其价带贡献最大的原子轨道，将这些相所表现出的各种电子结构聚类成不同的组。然后，借助机器学习，我们制定了新的化学规则，以预测每个相中价带最大值的位置。这些规则可用于设计具有能带收敛和高能谷简并性的能带结构。
