  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-square
$R^2$ accuracy is 99\% while the root mean square error of radii is 0.0332 \AA.
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 is consolidated
with the reputable Shannon's data and is made available online in a database
repository \url{https://cmd-ml.github.io/}.

Materials Science;Computational Physics