  In this study, we establish a basis for selecting similarity measures when
applying machine learning techniques to solve materials science problems. This
selection is considered with an emphasis on the distinctiveness between
materials that reflect their nature well. We perform a case study with a
dataset of rare-earth transition metal crystalline compounds represented using
the Orbital Field Matrix descriptor and the Coulomb Matrix descriptor. We
perform predictions of the formation energies using k-nearest neighbors
regression, ridge regression, and kernel ridge regression. Through detailed
analyses of the yield prediction accuracy, we examine the relationship between
the characteristics of the material representation and similarity measures, and
the complexity of the energy function they can capture. Empirical experiments
and theoretical analysis reveal that similarity measures and kernels that
minimize the loss of materials distinctiveness improve the prediction
performance.
