DFT study of RhTiP half Heusler semiconductor: Revealing its mechanical, optoelectronic, and thermoelectric properties

Dubey, Shubha; Abraham, Jisha A.; Dubey, Kumud; Sahu, Vineet; Modi, Anchit; Pagare, G.; Gaur, N.K.

doi:10.1016/j.physb.2023.415452

Cited by 15 publications

(1 citation statement)

References 48 publications

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“…Half-Heusler (hH) phases continue to draw unwavering interest due to various possible applications, e.g., in optoelectronics [1,2], thermoelectric devices [1,3,4], and ferromagnets [5]. The stable hH compounds are limited to the 18 valence electron systems, i.e., ternary phases with stoichiometry of 1:1:1, XYZ (space group no F43m), where Z is a main group element, while X and Y are transition metals.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Predictions for Half-Heusler Phases

Bilińska,

Winiarski

2023

Inorganics

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Machine learning models (Support Vector Regression) were applied for predictions of several targets for 18-electron half-Heusler phases: a lattice parameter, a bulk modulus, a band gap, and a lattice thermal conductivity. The training subset, which consisted of 47 stable phases, was studied with the use of Density Functional Theory calculations with two Exchange-Correlation Functionals employed (GGA, MBJGGA). The predictors for machine learning models were defined among the basic properties of the elements. The most optimal combinations of predictors for each target were proposed and discussed. Root Mean Squared Errors obtained for the best combinations of predictors for the particular targets are as follows: 0.1 Å (lattice parameters), 11–12 GPa (bulk modulus), 0.22 eV (band gaps, GGA and MBJGGA), and 9–9.5 W/mK (lattice thermal conductivity). The final results of the predictions for a large set of 74 semiconducting half-Heusler compounds were disclosed and compared to the available literature and experimental data. The findings presented in this work encourage further studies with the use of combined machine learning and ab initio calculations.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Predictions for Half-Heusler Phases

Bilińska,

Winiarski

2023

Inorganics

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Hydrostatic Pressure‐Tuning of Opto‐Electronic and Thermoelectric Properties Half‐Heusler Alloy RhTiP With DFT Analysis

Dixit,

Saxena,

Abraham

et al. 2024

Int J of Quantum Chemistry

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Utilizing DFT along with Boltzmann transport theory, the structural, elastic, electrical, optical, and thermoelectric properties of half‐Heusler compound RhTiP have been calculated in principle to examine the pressure effect in the range of 0–40 GPa. As pressure increases, the volume and normalized lattice parameter decreased. In addition to satisfying the Born stability criterion, which ensured the compound RhTiP “natural stability,” the zero pressure elastic constants and the pressure‐dependent elastic constants are positive up to 40 GPa. The band structure computations guarantee the semiconductor nature of RhTiP, as demonstrated by the presence of electronic band gap of 1.035 eV at zero pressure. Using the Voigt‐Reuss‐Hill (VRH) averaging scheme under pressure, we have determined the values of this compound's bulk modulus , shear modulus , Young's modulus , Pugh ratio , Poisson's ratio , and anisotropy factor . Because the bulk modulus responds linearly to pressure, the material's hardness increases as pressure rises. Additionally, under pressures up to 40 GPa, the optical characteristics of RhTiP, including their reflectivity, absorptivity, conductivity, dielectric constant, refractive index, and loss function, were assessed and discussed. Furthermore, the thermoelectric properties are also studied for the materials and supports the tunning of pressure. This study provides a gateway to how the optoelectronic and transport properties of cubic RhTiP could be tuned by employing external pressure.

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