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 . The magnetization in the whole 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 above 320 K.
1 More- Received 28 July 2020
- Revised 26 October 2020
- Accepted 12 January 2021
DOI:https://doi.org/10.1103/PhysRevMaterials.5.013806
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