Data assimilation method for experimental and first-principles data: Finite-temperature magnetization of (Nd, Pr, La, Ce)2(Fe, Co, Ni)14B

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 (Nd1-alpha-beta-gamma Pr alpha La beta Ce gamma)(2)(Fe1-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.

Automated summary

A data assimilation method was proposed to evaluate the finite-temperature magnetization of a permanent magnet over a high-dimensional composition space. Experimental data was integrated with first-principles calculation data to obtain the magnetization in the entire composition space at different temperatures. The results showed that Co doping did not enhance magnetization at low temperatures, but increased with increasing delta above 320 K.