Spinel nitride solid solutions: charting properties in the configurational space with explainable machine learning

Sánchez-Palencia, Pablo; Hamad, Said; Palacios, Pablo; Grau‐Crespo, Ricardo; Butler, Keith T.

doi:10.1039/d2dd00038e

Digital Discovery

2022

DOI: 10.1039/d2dd00038e

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Spinel nitride solid solutions: charting properties in the configurational space with explainable machine learning

Pablo Sánchez-Palencia

Said Hamad

Pablo Palacios

et al.

Abstract: Ab initio prediction of the variation of properties in the configurational space of solid solutions is computationally very demanding. We present an approach to accelerate these predictions via a combination...

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2024

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Cited by 2 publications

References 63 publications

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Spherical cluster method for ground state determination of site-disordered materials: Application to AgxBiyIx+3y

Barone,

Tuttle,

Khare

2024

Computational Materials Science

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Spherical cluster method for ground state determination of site-disordered materials: Application to AgxBiyIx+3y

Barone,

Tuttle,

Khare

2024

Computational Materials Science

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Effect of Changing and Combining Trivalent Metals in the Structural and Electronic Properties of Cu-Based Crystal Delafossite Materials

Cerutti Torres,

Sánchez-Palencia,

Jiménez-Sáez

et al. 2024

Crystals

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Cu-based ternary oxides with delafossite structure have received considerable attention in recent years for their versatility in a wide range of applications, among which is the possibility to use them in heterostructure solar cells as hole transport layers, due to their promising behavior as p-type conducting oxides. Ab initio calculations have been performed with density functional theory to investigate the role of the trivalent metal within the CuMO2 structure and the dependence of structural and electronic properties with the species (M = Al, Ga, In, Fe, Cr, Co, Sc, Y) occupying the site of the metal. Generalized Gradient Approximation also including a Hubbard term and nonlocal Heyd–Scuseria–Enzerhof screened hybrid functional schemes were tested and their results were compared. Excellent agreement with experimental lattice parameters and measured gaps have been found. The use of hybrid functionals in HSE approximation considerably improves the bandgaps when compared with the experimental results but takes considerable time to converge, hence the need to explore less demanding methodologies. Trends in the geometry as well as in the electronic properties are discussed, and the effect of mixing different metals (CuMxN1−xO2, M, N being the aforementioned elements) in the geometry and electronic properties of these delafossite materials is investigated. Due to the high cost of HSE calculations, especially when supercells are needed to model several x concentrations, statistical models and techniques based on machine learning have also been explored to predict HSE bandgap values from GGA and structural information.

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Spinel nitride solid solutions: charting properties in the configurational space with explainable machine learning

Abstract: Ab initio prediction of the variation of properties in the configurational space of solid solutions is computationally very demanding. We present an approach to accelerate these predictions via a combination...

Cited by 2 publications

References 63 publications

Spherical cluster method for ground state determination of site-disordered materials: Application to AgxBiyIx+3y

Spherical cluster method for ground state determination of site-disordered materials: Application to AgxBiyIx+3y

Effect of Changing and Combining Trivalent Metals in the Structural and Electronic Properties of Cu-Based Crystal Delafossite Materials

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