English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The Curie temperature ($T_C$) of binary alloy compounds consisting of 3$d$transition-metal and 4$f$ rare-earth elements is analyzed by a machine learningtechnique. We first demonstrate that nonlinear regression can accuratelyreproduce $T_C$ of the compounds. The prediction accuracy for $T_C$ ismaximized when five to ten descriptors are selected, with the rare-earthconcentration being the most relevant. We then discuss an attempt to utilize aregression-based model selection technique to learn the relation between thedescriptors and the actuation mechanism of the corresponding physicalphenomenon, i.e., $T_C$ in the present case.

A regression-based feature selection study of the Curie temperature of transition-metal rare-earth compounds: prediction and understanding