In computational material design, ionic radius is one of the most important physical parameters used to predict material properties. Motivated by the progress in computational materials science and material informatics, we extend the renowned Shannon's table from 475 ions to 987 ions. Accordingly, a rigorous machine learning (ML) approach is employed to extend the ionic radii table using all possible combinations of oxidation states (OS) and coordination numbers (CN) available in crystallographic repositories. An ionic-radius regression model for Shannon's database is developed as a function of the period number, the valence orbital configuration, OS, CN, and ionization potential. In the Gaussian process regression (GPR) model, the reached $R^2$ accuracy is 99% while the root mean square error of radii is 0.0332 Å. The optimized GPR model is then employed for predicting a new set of ionic radii for uncommon combinations of OS and CN extracted by harnessing crystal structures from materials project databases. The generated data are consolidated with the reputable Shannon's data and are made available online in a database repository.

中文翻译：

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使用机器学习扩展Shannon的离子半径数据库

在计算材料设计中，离子半径是用于预测材料性能的最重要的物理参数之一。受计算材料科学和材料信息学进步的推动，我们将著名的香农表从475个离子扩展到987个离子。因此，采用严格的机器学习（ML）方法，利用结晶库中可用的氧化态（OS）和配位数（CN）的所有可能组合来扩展离子半径表。香农数据库的离子半径回归模型是根据周期数，化合价轨道构型，OS，CN和电离势而开发的。在高斯过程回归（GPR）模型中，${\mathrm{[R}}^{\mathrm{2个}}$精度为99％，而半径的均方根误差为0.0332Å。然后，通过利用从材料项目数据库中提取的晶体结构，将优化的GPR模型用于预测OS和CN罕见组合的新离子半径集。生成的数据与信誉良好的Shannon数据合并在一起，并可以在线存储在数据库中。