Data Assimilation Method for Experimental and First-Principles Data: Finite-Temperature Magnetization of (Nd,Pr,La,Ce)${}_2$(Fe,Co,Ni)${}_{14}$B

Abstract

We propose a data-assimilation method for evaluating the finite-temperature magnetization of a permanent magnet over a high-dimensional composition space. Based on a general framework for constructing a predictor from two data sets including missing values, a practical scheme for magnetic materials is formulated in which a small number of experimental data in limited composition space are integrated with a larger number of first-principles calculation data. We apply the scheme to (Nd${}_{1-\alpha -\beta -\gamma }$Pr${}_{\alpha }$La${}_{\beta }$Ce${}_{\gamma }$)${}_2$(Fe${}_{1-\delta -\zeta }$Co${}_{\delta }$Ni${}_{\zeta }$)${}_{14}$B. The magnetization in the whole $(\alpha ,\beta ,\gamma ,\delta ,\zeta )$ space at arbitrary temperature is obtained. It is shown that the Co doping does not enhance the magnetization at low temperatures, whereas the magnetization increases with increasing $\delta$ above 320 K.

arXiv comment11 pages, 7 figures

References in corpus (4)

• Ab-initio calculations of exchange interactions, spin-wave stiffness constants, and Curie temperatures of Fe, Co, and Ni
• Quantum Theory of Rare-Earth Magnets
• Database of novel magnetic materials for high-performance permanent magnet development
• Bayesian optimization of chemical composition: a comprehensive framework and its application to $R$Fe${}_{12}$-type magnet compounds

Cited by in corpus (4)

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• Covariance Linkage Assimilation method for Unobserved Data Exploration