Accurate first-principle bandgap predictions in strain-engineered ternary III-V semiconductors

Abstract

Tuning the bandgap in ternary III-V semiconductors via modification of the composition or the strain in the material is a major approach for the design of optoelectronic materials. Experimental approaches screening a large range of possible target structures are hampered by the tremendous effort to optimize the material synthesis for every target structure. We present an approach based on density functional theory efficiently capable of providing the bandgap as a function of composition and strain. Using a specific density functional designed for accurate bandgap computation (TB09) together with a band unfolding procedure and special quasirandom structures, we develop a computational protocol efficiently able to predict bandgaps. The approach's accuracy is validated by comparison to selected experimental data. We thus map the phase space of composition and strain (we call this the ``bandgap phase diagram'') for several important III-V compound semiconductors: GaAsP, GaAsN, GaPSb, GaAsSb, GaPBi, and GaAsBi. We show the application of these diagrams for identifying the most promising materials for device design. Furthermore, our computational protocol can easily be generalized to explore the vast chemical space of III-V materials with all other possible combinations of III- and V-elements.

arXiv comment13 pages, 7 figures, GitHub (https://bmondal94.github.io/Bandgap-Phase-Diagram/)

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Cited by in corpus (1)

• Machine learning for accelerated bandgap prediction in strain-engineered quaternary III-V semiconductors