Structure classification of AB solids via machine learning

We explored the use of machine learning methods, specifically support vector machines and various forms of cross-validation, for the task of classifying the crystal structures of the octet AB solids. We partitioned a set of 75 solids into rocksalt and non-rocksalt structures and thus performed a binary classification task. We found that using the standard indices $(r_\sigma, r_\pi)$, suggested by St.\ John and Bloch several decades ago, enabled an average success in classification of $92\%$. Our main new result is our finding that using just $r_\sigma$ and the excess Born effective charge $\Delta Z_A$ of the A atom,computed by DFT, enabled an average success of $98\%$, prompting us to propose $(r_\sigma, \Delta Z_A)$ as a replacement for the St.\ John-Bloch pair. In general, we found that adding one or two other features to the St.\ John-Bloch pair, unless they include the excess Born effective charge, generally decreases the average success rate.