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

Duong Nguyen Nguyen, Tien Lam Pham, Viet Cuong Nguyen, Anh Tuan Nguyen, Hiori Kino, Takashi Miyake, Hieu Chi Dam

Research output: Contribution to journalConference articlepeer-review

8 Citations (Scopus)


The Curie temperature (T C) 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 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.

Original languageEnglish
Article number012009
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 2019 Oct 30
Externally publishedYes
Event30th IUPAP Conference on Computational Physics, CCP 2018 - Davis, United States
Duration: 2018 Jul 292018 Aug 2

ASJC Scopus subject areas

  • Physics and Astronomy(all)


Dive into the research topics of 'A regression-based model evaluation of the Curie temperature of transition-metal rare-earth compounds'. Together they form a unique fingerprint.

Cite this