First-principles investigations of structural, electronic, vibrational, and thermoelectric properties of half-Heusler VYGe(Y=Rh, Co, Ir)compounds

Solola, G.T.; Bamgbose, M.K.; Adebambo, Paul O.; Ayedun, Funmilayo; Adebayo, G.A.

doi:10.1016/j.cocom.2023.e00827

Cited by 5 publications

(3 citation statements)

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“…The results presented in this work for TiPdGe and TiPdPb are very similar to the literature data. Furthermore, the GGA predictions were also reported for VRhGe [37], indicating a of 5.73 Å, B of 186.1 GPa, and E g of 0.34 eV. While the lattice parameter calculated here is almost the same, the bulk modulus and band gap are slightly different from the literature data.…”

Section: Resultssupporting

confidence: 40%

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: Resultssupporting

confidence: 40%

Machine Learning-Based Predictions for Half-Heusler Phases

Bilińska,

Winiarski

2023

Inorganics

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“…However, PF p GGA of these systems was not explicitly discussed. The Ge-bearing phases, VRhGe and HfPdGe, were also analyzed in terms of TE performance at high temperature [40,41]. Considering antimonides, Kaur et al reported VRuSb as the favourable TE material [42].…”

Section: Resultsmentioning

confidence: 99%

Machine Learning-Based Predictions of Power Factor for Half-Heusler Phases

Bilińska,

Winiarski

2024

Crystals

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A support vector regression model for predictions of the thermoelectric power factor of half-Heusler phases was implemented based on elemental features of ions. The training subset was composed of 53 hH phases with 18 valence electrons. The target values were calculated within the density functional theory and Boltzmann equation. The best predictors out of over 2000 combinations regarded for the p-type power factor at room temperature are: electronegativity, the first ionization energy, and the valence electron count of constituent ions. The final results of support vector regression for 70 hH phases are compared with data available in the literature, revealing good ability to determine favorable thermoelectric materials, i.e., VRhGe, TaRhGe, VRuSb, NbRuAs, NbRuBi, LuNiAs, LuNiBi, TaFeBi, YNiAs, YNiBi, TaRuSb and NbFeSb. The results and discussion presented in this work should encourage further fusion of ab initio investigations and machine learning support, in which the elemental features of ions may be a sufficient input for reasonable predictions of intermetallics with promising thermoelectric performance.

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“…Many classes of materials have been proposed and studied in literature under the influence of point defects on their electronic [22], optical [23], and transport properties have been studied. These materials include heusler alloys [24][25][26][27], clathrates, skutterudites and two-dimensional chalcogenide materials. In two-dimensional (2D) materials, the structural defects that are likely to appear are generally of the form of point defects or edges [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Defect-induced modifications in electronic and thermoelectric properties of pentagonal PdX₂ (X = Se, S) monolayers

Sharma,

Nag,

Kumar

et al. 2024

Electron. Struct.

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The point defects induced in crystalline solids during the growth process unintentionally or doped intentionally after the growth process significantly modify their properties. The intentionally controlled doping of point defects in crystalline solids has been widely used to tune their properties. In this paper, we investigate the effect of vacancy and substitutional point defects on the electronic and thermoelectric properties of pentagonal PdX 2 (X= Se, S) monolayers using the density functional theory (DFT) and semi-classical Boltzmann transport theory. We find that the point defects in pentagonal PdX 2 (X= Se, S) monolayers modify their electronic structures. The contributions of d orbitals of Pd atoms and p orbitals of Se/S atoms are significantly affected due to the presence of point defects in the lattice. The defect states are appeared within the band gap region which effectively reduces the band gap of the monolayer. These defect states could be helpful in tuning the electrical and optical properties of the monolayer. The transport calculations show that the presence of thepoint defects in the lattice reduces the thermoelectric performance of PdX 2 monolayers. Both the Seebeck coefficient and electrical conductivity show deteriorated behaviour under the influence of point defects in the lattice. Thus, the influence of these defects must becarefully taken into account while fabricating these materials for practical applications.

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First-principles investigations of structural, electronic, vibrational, and thermoelectric properties of half-Heusler VYGe(Y=Rh, Co, Ir)compounds

Cited by 5 publications

References 50 publications

Machine Learning-Based Predictions for Half-Heusler Phases

Machine Learning-Based Predictions for Half-Heusler Phases

Machine Learning-Based Predictions of Power Factor for Half-Heusler Phases

Defect-induced modifications in electronic and thermoelectric properties of pentagonal PdX₂ (X = Se, S) monolayers

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First-principles investigations of structural, electronic, vibrational, and thermoelectric properties of half-Heusler VYGe(Y=Rh, Co, Ir)compounds

Cited by 5 publications

References 50 publications

Machine Learning-Based Predictions for Half-Heusler Phases

Machine Learning-Based Predictions for Half-Heusler Phases

Machine Learning-Based Predictions of Power Factor for Half-Heusler Phases

Defect-induced modifications in electronic and thermoelectric properties of pentagonal PdX2 (X = Se, S) monolayers

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Defect-induced modifications in electronic and thermoelectric properties of pentagonal PdX₂ (X = Se, S) monolayers