Benchmarking Coordination Number Prediction Algorithms on Inorganic Crystal Structures

Coordination numbers play a fundamental role in describing materials and conceptualizing their properties. On a large scale, algorithms to determine coordination numbers are useful for applications in machine learning and automatic structure analysis. We have developed a benchmarking framework called MaterialsCoord, an open-source software package for comparing algorithms on how well they determine coordination environments as described in the literature. A total of eight algorithms—seven of which are well-established and a novel algorithm, CrystalNN—are benchmarked on a diverse set of prototypical crystal structures. Apart from performance on the benchmark, we provide other analyses that may be important for implementation of these algorithms such as computational demand and sensitivity towards small perturbations that mimic thermal motion.