4.7 Article

Predictions on the Phase Constitution of SmCo7-XMx Alloys by Data Mining

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Review Physics, Condensed Matter

Machine Learning in Magnetic Materials

Georgios Katsikas et al.

Summary: This paper introduces machine learning methods applied to DFT simulation data to uncover connections between material structure, chemical composition, and magnetization. The research found that common elements in magnetic materials include Eu, Gd, Pu, Fe, and others, and materials of the same composition may exhibit different magnetization properties depending on their space group. Additionally, a neural network capable of predicting magnetization with a standard error of 8.3 x 10(-3) mu (B) angstrom(-3) is developed.

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