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Computationally Guided Discovery of Axis-Dependent Conduction Polarity in NaSnAs Crystals

Andrew M. Ochs, Prashun Gorai, Yaxian Wang, Michael R. Scudder, Karl G. Koster, Curtis E. Moore, Vladan Stevanović, Joseph P. Heremans, Wolfgang Windl, Eric S. Toberer, Joshua E. Goldberger

2021Chemistry of Materials28 citationsDOIOpen Access PDF

Abstract

Most electronic materials exhibit a single dominant charge carrier type, either holes or electrons, along all crystallographic directions. However, there are a small number of compounds, mostly metals, that exhibit simultaneous p-type and n-type conduction behavior along different crystallographic directions. We demonstrate that the experimental discovery of semiconductors with this axis-dependent conduction polarity can be facilitated by identifying a large anisotropy of either the electron or hole effective masses (m*) or both, providing the electron and hole masses dominate along different crystallographic directions. We calculated the layered semiconductor NaSnAs to have a lower electron m* in-plane than the cross-plane and a very large hole m* in-plane and small hole m* cross-plane. We established the growth of >3 mm-sized NaSnAs crystals via Sn flux and confirmed the band gap to be 0.65 eV, in agreement with theory. NaSnAs exhibits p-type thermopowers cross-plane and n-type thermopowers in-plane, confirming that the large anisotropy in the effective mass at the band edges is an excellent indicator for axis-dependent conduction polarity. Overall, this work shows that the discovery of semiconductors with such a phenomenon can be accelerated by computationally evaluating the anisotropic curvatures of the band edges, paving the way for their future discovery and application.

Topics & Concepts

AnisotropySemiconductorCondensed matter physicsElectronPolarity (international relations)Thermal conductionPlane (geometry)Effective mass (spring–mass system)Materials scienceBand gapPhysicsChemistryOpticsGeometryOptoelectronicsMathematicsQuantum mechanicsCellBiochemistryComposite materialAdvanced Thermoelectric Materials and DevicesElectronic and Structural Properties of OxidesMachine Learning in Materials Science
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