Abstract
Metal halide perovskites have attracted great attention in the past decade due to unique and tunable optical and electrical properties, which are promising candidates for various applications such as solar cells, light-emitting diodes, and laser cooling devices. For cubic perovskites, the lattice constant, a, representing the size of the unit cell, has a significant impact on the structural stability, bandgap structure, and thus materials performance. In this study, we develop the Gaussian process regression (GPR) model to shed light on the relationship among ionic radii, electronegativities, and lattice constants for cubic perovskite \(A_{2}XY_{6}\) compounds. A total of 79 samples with lattice constants ranging from 8.109 to 11.790 \(\mathring{\rm A}\) are examined. The model has a high degree of accuracy and stability, contributing to fast, robust, and low-cost estimations of lattice constants.
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Zhang, Y., Xu, X. Machine learning lattice constants from ionic radii and electronegativities for cubic perovskite \(A_{2}XY_{6}\) compounds. Phys Chem Minerals 47, 39 (2020). https://doi.org/10.1007/s00269-020-01108-4
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DOI: https://doi.org/10.1007/s00269-020-01108-4