First-Principles Investigation of Structural, Mechanical, Electronic and Optical Properties of Ba2MReO6 (M = Li, Na, K, and Rb) Double Perovskites

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Authors: Marcin Gackowski, Katarzyna Mądra-Gackowska, MA Khan, Łukasz Szeleszczuk
Cited by: 1
References: 56

Abstract

The growing demand for efficient, stable, and environmentally friendly materials for next-generation optoelectronic and photovoltaic applications has attracted significant interest in double perovskite compounds. First-principles density functional theory (DFT) calculations were performed to systematically investigate the structural, mechanical, electronic, and optical properties of Ba2MReO6 (M = Li, Na, K, and Rb) double perovskites. Structural optimization confirms that all compounds crystallize in the cubic Fm3̅m symmetry. The thermodynamic and geometric stability of the series is checked with negative formation energies and tolerance factor analyses (t, μ, τ). Mechanical analysis confirms that all compounds are mechanically stable; Ba2LiReO6 is the stiffest, while Ba2RbReO6 shows moderate stiffness with the highest ductility. Furthermore, ab initio molecular dynamics (AIMD) simulations at room temperature confirm the dynamical stability of all compounds, with negligible fluctuations in total energy under thermal conditions. The calculated band structures using both GGA-PBE and HSE06 hybrid functionals reveal that all compounds possess indirect band gaps, with HSE06 values of 2.236 eV for Ba2LiReO6, 2.133 eV for Ba2NaReO6, 2.116 eV for Ba2KReO6, and 1.395 eV for Ba2RbReO6. Optical measurements indicate that it is highly polarizable by dielectric polarizability, has high absorption coefficients (approximately 106 cm−1), and has large optical conductivity in the UV, with large inter-band interactions between 2 and 4 eV. The suitable band gap and favorable optical characteristics suggest that Ba2RbReO6 is the most promising candidate for photovoltaic and solar-cell applications.

Fields of Study & Subjects

Heusler alloys: electronic and magnetic properties Magnetic and transport properties of perovskites and related materials Machine Learning in Materials Science

Publication details

Journal / source
International Journal of Molecular Sciences
Publisher
MDPI AG
Volume
27
Issue
14
Pages
6186–6186
Publication date
10 July 2026
Language
EN