Anions’ Radii — New data points calibrated to match Shannon’s table

Abstract

Ionic radii play a key descriptor role in the field of material informatics and crystallography. Traditionally, improving the widely used Shannon’s radii dataset has primarily involved extending the cation radii since the original data was mostly cation-focused – thereby limiting its applicability. Accordingly, we have developed a method to estimate anion radii using a self-consistent calibration approach based on interatomic distances in binary compounds. This improvement shall enhance the precision of ionic radii-based descriptors, allowing for the exploration of a broader range of compounds beyond the usual oxides and fluorides. In this study, we conducted a detailed calibration protocol to enhance Shannon’s consolidated ionic radii table by integrating new anion entries and ensuring consistency with the established data. We employed a low-order regression model on the reference anions

,

, and

to accurately estimate their radii in missing coordination numbers (five other points). These values proved crucial for recalibrating the set of key reference cations’ radii, which included

,

,

,

,

, and

, across coordination numbers 4, 6, and 8. We used recently updated and accurate interatomic distances from highly symmetric cubic binary structures in the Materials Project database to ensure this recalibration. Consequently, the adjusted cationic radii matched closely with Shannon’s original values, with deviations less than 5%, highlighting the accuracy of our approach. These calibrated cations were then used to derive new anion entries for binary and highly symmetric compounds expanding the data the database from 16 anion in Shannon’s to 33 in the proposed work. The implemented method resulted in 17 new anion configurations, namely

,

,

,

,

,

,

,

,

,

,

,

,

,

,

,

, and

, and updated six existing configurations, namely

,

,

,

,

, and

. Our results have been integrated into Shannon’s updated ionic radii table, accessible at https://cmd-ml.github.io/, providing a robust data set for ongoing and future research in crystallography and materials engineering.