Double perovskite oxides have attracted great attention in the past decade due to their unique and versatile material properties. The lattice constant, a, as the only variable parameter among the six parameters in the cubic structure, has a significant impact on the structural stability, electronic structure, magnetic ordering, and thus material performance. In this work, a Gaussian process regression (GPR) model is developed to elucidate the statistical relationship among ionic radii, electronegativities, oxidation states, and lattice constants for cubic perovskite A₂²⁺BB′O₆ compounds. A total of 147 samples with lattice constants ranging from 7.700 Å to 8.890 Å are explored. The modeling approach demonstrates a high degree of accuracy and stability, contributing to efficient and low-cost estimations of lattice constants.

中文翻译：

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立方钙钛矿A22 + BB′O6化合物的机器学习晶格常数

在过去的十年中，双重钙钛矿氧化物因其独特而通用的材料性能而备受关注。晶格常数a是立方结构中六个参数中唯一的可变参数，它对结构稳定性，电子结构，磁有序性以及材料性能具有重大影响。在这项工作中，建立了高斯过程回归（GPR）模型以阐明立方钙钛矿A ₂ ²⁺ BB'O _6的离子半径，电负性，氧化态和晶格常数之间的统计关系。化合物。总共147个样品的晶格常数范围为7.700Å至8.890Å。该建模方法证明了高度的准确性和稳定性，有助于有效且低成本地估计晶格常数。