Open AccessA regression-based model evaluation of the Curie temperature of transition-metal rare-earth compounds
Author(s) -
Duong-Nguyen Nguyen,
Tien-Lam Pham,
Viet-Cuong Nguyen,
Anh-Tuan Nguyen,
Hiori Kino,
Takashi Miyake,
H. van Dam
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1290/1/012009
Subject(s) - mathematics , kernel (algebra) , regression analysis , latent variable , rare earth , regression , linear regression , statistics , discrete mathematics , chemistry , mineralogy
The Curie temperature ( T C ) of RT binary compounds consisting of 3 d transition-metal ( T ) and 4 f 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 T C . 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 T C of the RT compounds. The relevance of descriptive variables for predicting T C are systematically investigated. The results indicate that the rare-earth concentration is the most relevant variable in the T C 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., T C in the present case.