Coercivity influence of nanostructure in SmCo-1:7 magnets: Machine learning of high-throughput micromagnetic data

Around 17,000 micromagnetic simulations were performed with a wide variation of geometric and magnetic parameters of different cellular nanostructures in the samarium-cobalt-based 1:7-type (SmCo-1:7) magnets. A forward prediction neural network (NN) model is trained to unveil the influence of these...

Full description

Saved in:
Bibliographic Details
Main Authors Yang, Yangyiwei, Kühn, Patrick, Fathidoost, Mozhdeh, Adabifiroozjaei, Esmaeil, Xie, Ruiwen, Foya, Eren, Ohmer, Dominik, Skokov, Konstantin, Molina-Luna, Leopoldo, Gutfleisch, Oliver, Zhang, Hongbin, Xu, Bai-Xiang
Format Journal Article
LanguageEnglish
Published 06.08.2024
Subjects
Physics - Computational Physics
Physics - Disordered Systems and Neural Networks
Physics - Materials Science
Online AccessGet full text

Cover

More Information
Summary:Around 17,000 micromagnetic simulations were performed with a wide variation of geometric and magnetic parameters of different cellular nanostructures in the samarium-cobalt-based 1:7-type (SmCo-1:7) magnets. A forward prediction neural network (NN) model is trained to unveil the influence of these parameters on the coercivity of materials, along with the sensitivity analysis. Results indicate the important role of the 1:5-phase in enhancing coercivity. Moreover, an inverse design NN model is obtained to suggest the nanostructure for a queried coercivity.
DOI:10.48550/arxiv.2408.03198