A study of anisotropic thermoelectric properties of bulk Germanium Sulfide in its Pnma phase: a combined first-principles and machine-learning approach. (1st December 2022)

• Record Type: Journal Article
• Authors: Rakshit, Medha; Nath, Subhadip; Chowdhury, Suman; Mondal, Rajkumar; Banerjee, Dipali; Jana, Debnarayan
• Is Part Of: Physica scripta. Volume 97:Number 12(2022)
• Publication Date: 2022-12-01
• Subjects: metal chalcogenides -- thermoelectric properties -- DFT calculations -- Germanium Sulfide -- machine-learning approach; Physics -- Periodicals; 530.05
• DOI: 10.1088/1402-4896/ac9be4
• Languages: English
• ISSNs: 0031-8949

Abstract

Abstract: This work reports a detailed and systematic theoretical study of the anisotropic thermoelectric properties of bulk Germanium Sulfide (GeS) in its orthorhombic Pnma phase. Density functional theory (DFT), employing the generalized gradient approximation (GGA), has been used to examine the structural and electronic band structure properties of bulk GeS. Electronic transport properties have been studied by solving semiclassical Boltzmann transport equations. A machine-learning approach has been used to estimate the temperature-dependent lattice part of thermal conductivity. The study reveals that GeS has a direct band gap of 1.20 eV. Lattice thermal conductivity is lowest along crystallographic a-direction, with a minimum of ∼0.98 Wm −1 K −1 at 700 K. We have obtained the maximum figure of merit ( ZT ) ∼ 0.73 at 700 K and the efficiency ∼7.86% in a working temperature range of 300 K–700 K for pristine GeS along crystallographic a-direction.