Big data approach for effective ionic radii

Effective ionic radii are a useful tool for researchers in many disciplines. These radii are usually determined from interatomic distances and the values tabulated by Shannon and Prewitt are widely used since 1969. Here, we present a modern approach based on automated queries to materials databases followed by categorization and tabulation. This method is used to augment the available set of effective ionic radii by providing radii for missing anionic oxidation states and coordination numbers. Our approach proves to yield results that are consistent with known values for anions with well-established oxidation states. More exotic cases result in larger uncertainties due to the smaller amount of available structural data. Nevertheless, the provided estimates give reasonable values for cases that are untabulated at present. Furthermore, our approach is designed to continuously improve together with the growth of available databases. Furthermore, small tweaks of the presented application will allow us to also complement or revise ionic radii of cations. Thus, our approach is designed to update the old and useful concept of effective ionic radii, bringing it to modern days. Program Title: extend_anionic_radii.py Program Files doi: http://dx.doi.org/10.17632/sp4c25z8pn.1 Licensing provisions: LGPL-3.0 Programming language: Python3 Supplementary material: The database of ionic radii is available online, preliminary under https://extend-sp-radii-new.herokuapp.com. Nature of problem: Determine the effective ionic radius for elements in anionic oxidation states (OS), depending on the coordination number (CN). This is done by harvesting the Materials Project (MP)[1] database. Newly found radii are adopted in a way that they coincide with reference values of the established Shannon–Prewitt (SP)[2] table for ionic radii. Solution method: For an element X in oxidation state OS, find all binary compounds contained in the MP that are composed by X and alkali or alkaline-earth metals. Determine OS and CN for all elements in each of the identified binary compounds (assume OS=+1 and +2 for alkali and alkaline-earth metals, respectively). Determine the nearest neighbor distances between the elements. From the obtained information and the SP reference values, compute the effective ionic radius of X, depending on OS and CN. [[1]] A. Jain, S. P. Ong, G. Hautier, W. Chen, W. D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner,G. Ceder, K. A. Persson, Commentary: The materials project: A materials genome approach toaccelerating materials innovation, APL Mater. 1 (1). doi:10.1063/1.4812323. [[2]] R. D. Shannon, C. T. Prewitt, Effective ionic radii in oxides and fluorides, Acta Cryst. B25 (1454) (1969)925–946.