Optimization of 2D Transition Metal Dichalcogenides Using Electronic Structure and Thermoelectric Coefficient Calculations

Transition metal dichalcogenides (TMDs) and doped layered compounds are emerging classes of two-dimensional (2D) materials with technological potential for their use in photovoltaic devices. These materials exhibit useful electronic, optical, and thermal properties with tunable direct bandgaps. However, there is a need to develop novel nanostructure design strategies to improve the thermoelectric performance and <i>p</i>-type characteristics of these materials. Computational materials design can assist in these efforts and accelerate materials development. To address this need, we have performed plane-wave density functional theory (DFT) calculations for a series of transition metal dichalcogenide thin films (MoTe₂, WTe₂) and bismuth-antimony alloys (BiSb, BiTe). In particular, we are focusing on TMDs that contain telluride, given their particular importance for device studies. We used DFT to focus on the tolerance of the properties of these materials to variations in polymorph, composition, and defect type. We have validated the DFT predictions against experimental and published computational work. We will also present the predictions of DFT-generated thermoelectric coefficients using the open-source BoltzTraP2 codebase, based on the Boltzmann transport equation. These coefficients include electrical conductivity, thermal conductivity, Seebeck coefficient, power factor, and figure of merit. We will show how variation of the band gap and density of states (DOS) in all structures will cover both semiconductors and metals with thermoelectric properties in the expected ranges of previous observations. Our goal is to use these calculated thermoelectric properties to gauge which materials are preferable for use in device fabrication and assist with optimizing the performance of these 2D materials. We will use a Bayesian optimization machine learning algorithm (Physical Analytics Pipeline) to search the large combinatorial space comprised of different compositional alloys and determine which combinations yield particularly noteworthy <i>p</i>-type characteristics.