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.

中文翻译：

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来自立方钙钛矿 $$A_{2}XY_{6}$$ 化合物的离子半径和电负性的机器学习晶格常数

金属卤化物钙钛矿由于独特且可调谐的光学和电学性质在过去十年中引起了极大的关注，是太阳能电池、发光二极管和激光冷却装置等各种应用的有前途的候选材料。对于立方钙钛矿，晶格常数 a 代表晶胞的大小，对结构稳定性、带隙结构以及材料性能有重大影响。在这项研究中，我们开发了高斯过程回归 (GPR) 模型，以阐明立方钙钛矿 $$A_{2}XY_{6}$$ 化合物的离子半径、电负性和晶格常数之间的关系。检查了总共 79 个晶格常数范围从 8.109 到 11.790 $$\mathring{\rm A}$$ 的样品。该模型具有高度的准确性和稳定性，有助于快速、