A regression-based feature selection study of the Curie temperature of transition-metal rare-earth compounds: prediction and understanding
arXiv: Materials Science(2017)
摘要
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 learning technique. We first demonstrate that nonlinear regression can accurately reproduce $T_C$ of the compounds. The prediction accuracy for $T_C$ is maximized when five to ten descriptors are selected, with the rare-earth concentration being the most relevant. We then discuss an attempt to utilize a regression-based model selection technique to learn the relation between the descriptors and the actuation mechanism of the corresponding physical phenomenon, i.e., $T_C$ in the present case.
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关键词
curie temperature,feature selection study,transition-metal transition-metal,regression-based,rare-earth
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