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

2017/05/02 by Hieu Chi Dam, Dam, Hieu Chi, Viet Cuong Nguyen +11
Physics and Astronomy · #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci) #cond-mat.mtrl-sci

paper · pdf · doi:10.48550/arxiv.1705.00978

4 pages

arxiv created 2017/05/03 · arxiv updated 2017/05/04

Abstract

The Curie temperature (T_C) of binary alloy compounds consisting of 3d transition-metal and 4f 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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