Data assimilation method for experimental and first-principles data: Finite-temperature magnetization of (Nd,Pr,La,Ce)2(Fe,Co,Ni)14B
Data assimilation method for experimental and first-principles data: Finite-temperature magnetization of (Nd,Pr,La,Ce)2(Fe,Co,Ni)14B
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DOI：
10.1103/physrevmaterials.5.013806
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发表时间：
2020-07
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影响因子：
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通讯作者：
Y. Harashima;Keiichi Tamai;S. Doi;M. Matsumoto;H. Akai;N. Kawashima;Masaaki Ito;N. Sakuma;A. Kato;T. Shoji;T. Miyake
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作者：
Y. Harashima;Keiichi Tamai;S. Doi;M. Matsumoto;H. Akai;N. Kawashima;Masaaki Ito;N. Sakuma;A. Kato;T. Shoji;T. Miyake
我们提出了一种数据同化方法，用于评估高维成分空间上永磁体的有限温度磁化强度。基于从两个包含缺失值的数据集构建预测器的总体框架，制定了磁性材料的实用方案，其中将有限组成空间中的少量实验数据与大量第一原理计算数据相结合。我们将该方案应用于 (Nd$_{1-\alpha-\beta-\gamma}$Pr$_{\alpha}$La$_{\beta}$Ce$_{\gamma}$)$_{2}$(Fe$_{1-\delta-\zeta}$Co$_{\delta}$Ni$_{\zeta}$)$_{14}$B。获得任意温度下整个$(\alpha,\beta,\gamma,\delta,\zeta)$空间的磁化强度。结果表明，Co 掺杂不会增强低温下的磁化强度，而在 320 K 以上，磁化强度随着 $\delta$ 的增加而增加。
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.