The Principal Component Analysis as a tool for predicting the mechanical properties of Perovskites and Inverse Perovskites

Abstract

Cubic perovskites and inverse perovskites have been largely studied in the last years due to their wide interesting physical and chemical properties. However, a little works has been done on their thermal properties, mechanical properties such as strength, stiffness, corrosion resistance. In this work, we would like to demonstrate that perovskites and inverse perovskites could be used to design high-strength ultra-hard coatings materials by fabricating a layered structure of two materials with the same crystal structure. Comparatively to other authors, in our works we have used a multivariate technique based on principal component analysis (PCA) and the partial least square regression (PLS-R). We demonstrate that the score plots allow us to clearly identify the more interesting compounds comparatively one to the other. On the other hand, we have proposed an approach based on the Koehler method in order to predict which perovskites and inverse perovskites have the potential to achieve high hardness and fracture toughness for use as a thermal barrier coating (TBC). We propose 10 among the 129 perovskites and inverse perovskites studied have the potential to be used as thermal barrier coatings. These findings could be used to develop novel multilayer ultra-hard coating materials based on perovskites.