Material descriptors for thermoelectric performance of narrow-gap semiconductors and semimetals
Simple descriptors to search for low-temperature thermoelectric materials.
Chemistry↗
Engineering topics
Toriyama, Michael
Publications and source records attributed to Toriyama, Michael.
Using phase boundary mapping to resolve discrepancies in the Mg 2 Si–Mg 2 Sn miscibility gap
Mg 2 Si–Mg 2 Sn compositions within the Mg–Si–Sn materials system have potential as inexpensive, efficient thermoelectrics. These compositions lie specifically along the pseudobinary line with compositions of Mg 2 Si 1-x Sn x . The alloying and possible nanostructuring within the miscibility gap could further increase the thermoelectric figure of merit (zT) for these materials. However, the solubility limits of the miscibility gap differ greatly in the literature. Such a discrepancy could be a result of differing Mg-compositions due to excess magnesium added during sample annealing. To define these limits better and explain the change in proposed solubility limits based on magnesium content, the three-phase regions on either side of the pseudobinary phase region are phase boundary mapped and defect energy calculations are performed. This study presents a new understanding of the Mg–Si–Sn ternary phase diagram around the pseudobinary phase region. The solubility limits on either side of the pseudobinary should be essentially identical between the Mg-rich and Mg-poor three-phase regions unless the system temperature is brought above about 565 °C, at which eutectic liquid Mg 0.9 Sn 0.1 forms. This creates a second Mg-rich three-phase region which intersects the pseudobinary with a lower Sn solubility. Thus, samples prepared along the pseudobinary line are not well-defined thermodynamically when excess magnesium is added. Excess Mg can push the system into a new three phase region with Mg 2 Si 1-x Sn x composition different from that of the true miscibility gap. This understanding presents new guidelines for evaluating the miscibility gap and assists strategies for microstructure engineering and thermoelectric material processing.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Machine learning defect properties in Cd-based chalcogenides
Impurity energy levels in the band gap can have serious consequences for a semiconductor's performance as a photovoltaic absorber. Data-driven approaches can help accelerate the prediction of point defect properties in common semiconductors, and thus lead to the identification of potential deep lying impurity states. In this work, we use density functional theory (DFT) to compute defect formation energies and charge transition levels of hundreds of impurities in CdX chalcogenide compounds, where X = Te, Se or S. We apply machine learning techniques on the DFT data and develop on-demand predictive models for the formation energy and relevant transition levels of any impurity atom in any site. The trained ML models are general and accurate enough to predict the properties of any possible point defects in any Cd-based chalcogenide, as we prove by testing on a few selected defects in mixed chalcogen compounds CdTe 0.5 Se 0.5 and CdSe 0.5 S 0.5 . The ML framework used in this work can be extended to any class of semiconductors.
density functional theory↗