Abstract Crystal structure classification of perovskites (ABO3) is done using the Light Gradient Boosting Machine (Light GBM) algorithm. In this work, we have identified features such as electronegativity, ionic radius, valence, and bond lengths of A-O and B-O pairs that enable a priori crystal structure prediction. We have taken 5329 ABO3 perovskites and applied the proposed model to 675 compounds. It successfully categorized the compounds into cubic, tetragonal, orthorhombic, and rhombohedral structures with 80.3% best accuracy using 5-fold cross-validation. Therefore, the model can be used as a preliminary, fast, and inexpensive method to classify perovskites into their respective crystal systems. Feature importance graph and SHapley Additive exPlanations (SHAP) are used in feature ranking and crystal structure prediction. These composition-structure predictions will find applications in ceramic engineering and solid-state chemistry.

中文翻译：

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通过机器学习对 ABO3 钙钛矿进行晶体结构分类

摘要 钙钛矿 (ABO3) 的晶体结构分类是使用光梯度提升机 (Light GBM) 算法完成的。在这项工作中，我们已经确定了能够进行先验晶体结构预测的 AO 和 BO 对的电负性、离子半径、价数和键长等特征。我们采用了 5329 个 ABO3 钙钛矿并将所提出的模型应用于 675 种化合物。它使用 5 倍交叉验证成功地将化合物分类为立方、四方、正交和菱面体结构，最佳准确率为 80.3%。因此，该模型可用作将钙钛矿分类为各自晶体系统的初步、快速且廉价的方法。特征重要性图和 SHapley Additive exPlanations (SHAP) 用于特征排序和晶体结构预测。