Data Assimilation Method for Experimental and First-Principles Data: Finite-Temperature Magnetization of (Nd,Pr,La,Ce)$_{2}$(Fe,Co,Ni)$_{14}$B

TL;DR

This paper introduces a data-assimilation method combining experimental and first-principles data to evaluate the temperature-dependent magnetization of complex magnetic materials across a high-dimensional composition space.

Contribution

A novel data-assimilation framework that integrates limited experimental data with extensive first-principles calculations for magnetic property prediction.

Findings

Co doping does not enhance low-temperature magnetization.

Magnetization increases with Co content above 320 K.

The method enables prediction of magnetization across composition and temperature.

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.