Accurate first principles band gap predictions in strain engineered ternary III-V semiconductors

Badal, Mondal,; Marcel, Kröner,; Hepp, Thilo; Volz, Kerstin; Tonner, Ralf

doi:10.1103/physrevb.108.035202

Phys. Rev. B

2023

DOI: 10.1103/physrevb.108.035202

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Accurate first principles band gap predictions in strain engineered ternary III-V semiconductors

Mondal, Badal

Kröner, Marcel

Thilo Hepp

et al.

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

Mondal,

Westermayr,

Tonner-Zech

2023

The Journal of Chemical Physics

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Quaternary III–V semiconductors are one of the most promising material classes in optoelectronics. The bandgap and its character, direct or indirect, are the most important fundamental properties determining the performance and characteristics of optoelectronic devices. Experimental approaches screening a large range of possible combinations of III- and V-elements with variations in composition and strain are impractical for every target application. We present a combination of accurate first-principles calculations and machine learning based approaches to predict the properties of the bandgap for quaternary III–V semiconductors. By learning bandgap magnitudes and their nature at density functional theory accuracy based solely on the composition and strain features of the materials as an input, we develop a computationally efficient yet highly accurate machine learning approach that can be applied to a large number of compositions and strain values. This allows for a computationally efficient prediction of a vast range of materials under different strains, offering the possibility of virtual screening of multinary III–V materials for optoelectronic applications.

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

Mondal,

Westermayr,

Tonner-Zech

2023

The Journal of Chemical Physics

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Accurate first principles band gap predictions in strain engineered ternary III-V semiconductors

Cited by 1 publication

References 109 publications

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

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

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