The F1 scores for coordination number here (over a test set combining OQMD and MP structures) are slightly lower than those in previous work using artificial neural networks for classification on a smaller data set of only MP structures (see Table of Ref. ); the better performance of neural networks may also be explained by their larger parameter space. We note that a direct performance comparison with earlier work by Zheng and Chen et al. is difficult, due to differences in the targeted property -specific discrete coordination numbers here, compared to continuously weighted coordination motifs in Ref. . Their high accuracies of > 0.9 for the same absorbing elements suggest that weighted coordinations may be a better choice of target for training future ML models.