Publication date: 10th April 2024
Face the current climate emergency, solid-state batteries have been attracting significant attention due to a plethora of potential advantages, such as energy density gains, reduced costs, and safety enhancements. [1] In recent years, Li-rich anti-perovskites have stood out as promising solid electrolyte candidates as they combine high ionic conductivity, stability against Li metal anodes and structural versatility. [2],[3]
Here, defect simulations are used to explore the energetics of defect formation in a range of LixOXy (X = Cl or Br; x = 3–6; y = 1–4) anti-perovskites with zero- to three-dimensional structures, in a work we recently published in the Royal Society of Chemistry’s journal Energy Advances (Energy Adv., 2023,2, 653). Defect calculations are conducted utilising the Mott−Littleton approximation. Long molecular dynamics runs are carried out to assess ion transport in these materials at a range of temperatures (200–800 K). The range of defects investigated includes full, Li-halide and Li-O Schottky defects and Li Frenkel defects. Our calculations predict that whereas almost all these materials present Li-halide Schottky defect pairs as dominant native defects, Cl interstitials are the dominant type of intrinsic disorder in Li6OCl4. We find that the formation of the great majority of defect types is energetically more favourable in the LixOCly series compared to the equivalent structures in the LixOBry set, potentially leading to enhanced Li-ion transport in these materials. We also report that the concentrations of halide Frenkel defects in the LixOBry set are lower than expected in the three and two-dimensional structures, based on the LixOCly series findings for their counterparts. Our molecular dynamics simulations reveal the strong connection between Li-ion dynamics and dimensionality in these anti-perovskite materials, where increased Li-ion diffusion and decreased activation energy can be seen as dimensionality is reduced. Density functional theory simulations and machine learning studies are currently ongoing to further assess ion transport and interfaces in these materials.
AC. C. D. and J. A. D. thank the Newcastle University Academic Track (NUAcT) Fellowship Scheme for financial support. J. A. D. gratefully acknowledges the Engineering and Physical Sciences Research Council (EPSRC, EP/V013130/1) for funding. G. E. R. acknowledges support from the EPSRC CDT in Renewable Energy Northeast Universities (ReNU) for funding (EP/S023836/1). Via membership of the UK's HEC Materials Chemistry Consortium, which is funded by the EPSRC (EP/R029431), this work used the ARCHER2 UK National Supercomputing Service. The Rocket High Performance Computing service at Newcastle University was also used.
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