Machine learning-aided Genetic algorithm in investigating the structure–property relationship of SmFe12-based structures

Abstract

We investigate the correlation between geometrical information, stability, and magnetization of SmFe12-based structures using machine learning-aided genetic algorithm structure generation and first-principle calculation. In parallel with structure generation inherited using the USPEX program, a pool of structures is created for every population using the sub-symmetry perturbation method. A framework using embedded orbital field matrix representation as structure fingerprint and Gaussian process as a predictor has been applied to ranking the most potential stability structures. As a result, the original structure SmFe12 with the well-known tetragonal I4/mmm symmetry is investigated with a parabolic dependence between formation energy and its magnetization by continuous distortions of the unit-cell lattice parameter and individual sites. Notably, a SmFe12 structure with I4/mmm symmetry is found with 7.5% increasing magnetization while keeping the similar formation energy with the most stable structures in this family. With SmFe11CoN family, structures with N interstitial position in the center of Sm and Fe octahedron show outperform all other structures in both ability of stabilization and remaining high magnetization of the original structure. Finally, further investigation using metric learning embedding space brings valuable insight into the correlation between geometrical arrangement, stability, and magnetization of this structure family.