A first-principles study of the structural, electronic, optical, and vibrational properties for paramagnetic half-Heusler compound TiIrBi by GGA and GGA + mBJ functional

Candan, A.; Kushwaha, A.K.

doi:10.1016/j.mtcomm.2021.102246

Cited by 9 publications

(2 citation statements)

References 68 publications

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“…One may explain this issue by considering that the band structures of these materials, the fact that the number of bisimides in our training set is limited, and the performance of our SVR approach is poor for narrow band gap materials. The reported a for TiIrBi ranged from 6.309 to 6.358 Å, depending on the alloy model [57], which is in good accordance with the value predicted here. The range of reported B was from 104.4 up to 123.7 GPa, which is slightly lower than our result.…”

Section: Resultssupporting

confidence: 90%

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: 90%

Machine Learning-Based Predictions for Half-Heusler Phases

Bilińska,

Winiarski

2023

Inorganics

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“…The wide application of thermoelectric (TE) materials is desirable due to the accumulating heat losses of various origins (e.g., energy and automotive industries). Despite numerous comprehensive studies [1][2][3][4][5][6][7], the subject of TE performance among half-Heusler (hH) phases is still an interesting area of research. Many alloys and whole families of compounds (e.g., tellurides) were not investigated up to now [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

High-Throughput Exploration of Half-Heusler Phases for Thermoelectric Applications

Bilińska,

Winiarski

2023

Crystals

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As a result of the high-throughput ab initiocalculations, the set of 34 stable and novel half-Heusler phases was revealed. The electronic structure and the elastic, transport, and thermoelectric properties of these systems were carefully investigated, providing some promising candidates for thermoelectric materials. The complementary nature of the research is enhanced by the deformation potential theory applied for the relaxation time of carriers (for power factor, PF) and the Slack formula for the lattice thermal conductivity (for figure of merit, ZT). Moreover, two exchange-correlation parametrizations were used (GGA and MBJGGA), and a complete investigation was provided for both p- and n-type carriers. The distribution of the maximum PF and ZT for optimal doping at 300 K in all systems was disclosed. Some chemical trends in electronic and transport properties were discussed. The results suggest TaFeAs, TaFeSb, VFeAs, and TiRuAs as potentially valuable thermoelectric materials. TaFeAs revealed the highest values of both PF and ZT at 300 K (PFp = 1.67 mW/K2m, ZTp = 0.024, PFn = 2.01 mW/K2m, and ZTp = 0.025). The findings presented in this work encourage further studies on the novel phases, TaFeAs in particular.

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Impact of calcination temperature on the structural, electrical, and dielectric properties of sol–gel synthesized Ni0.6Mg0.2Co0.2FeCrO4 spinel ferrites

Bouazizi

Hcini

Khirouni

et al. 2023

J Mater Sci: Mater Electron

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A first-principles study of the structural, electronic, optical, and vibrational properties for paramagnetic half-Heusler compound TiIrBi by GGA and GGA + mBJ functional

Cited by 9 publications

References 68 publications

Machine Learning-Based Predictions for Half-Heusler Phases

Machine Learning-Based Predictions for Half-Heusler Phases

High-Throughput Exploration of Half-Heusler Phases for Thermoelectric Applications

Impact of calcination temperature on the structural, electrical, and dielectric properties of sol–gel synthesized Ni0.6Mg0.2Co0.2FeCrO4 spinel ferrites

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A first-principles study of the structural, electronic, optical, and vibrational properties for paramagnetic half-Heusler compound TiIrBi by GGA and GGA + mBJ functional

Cited by 9 publications

References 68 publications

Machine Learning-Based Predictions for Half-Heusler Phases

Machine Learning-Based Predictions for Half-Heusler Phases

High-Throughput Exploration of Half-Heusler Phases for Thermoelectric Applications

Impact of calcination temperature on the structural, electrical, and dielectric properties of sol–gel synthesized Ni0.6Mg0.2Co0.2FeCrO4 spinel ferrites

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A first-principles study of the structural, electronic, optical, and vibrational properties for paramagnetic half-Heusler compound TiIrBi by GGA and GGA + mBJ functional