A regression-based model evaluation of the Curie temperature of transition-metal rare-earth compounds

Abstract

The Curie temperature (TC) of RT binary compounds consisting of 3d transition-metal (T ) and 4f rare-earth elements (R) is analyzed systematically by a developed machine learning technique called kernel regression-based model evaluation. Twenty-one descriptive variables were designed assuming completely obtained information of the TC. Multiple kernel regression analyses with different kernel types: cosine, linear, Gaussian, polynomial, and Laplacian kernels were implemented and examined. All possible descriptive variable combinations were generated to construct the corresponding prediction models. As a result, by appropriate combinations between descriptive variable sets and kernel formulations, we demonstrate that a number of kernel regression models can accurately reproduce the TC of the RT compounds. The relevance of descriptive variables for predicting TC are systematically investigated. The results indicate that the rare-earth concentration is the most relevant variable in the TC phenomenon. We demonstrate that the regression-based model selection technique can be applied to learn the relationship between the descriptive variables and the actuation mechanism of the corresponding physical phenomenon, i.e., TC in the present case.