Optimizing machine learning models for granular NdFeB magnets by very fast simulated annealing.

ABSTRACT: The macroscopic properties of permanent magnets and the resultant performance required for real implementations are determined by the magnets' microscopic features. However, earlier micromagnetic simulations and experimental studies required relatively a lot of work to gain any complete and comprehensive understanding of the relationships between magnets' macroscopic properties and their microstructures. Here, by means of supervised learning, we predict reliable values of coercivity (μ₀H_c) and maximum magnetic energy product (BH_max) of granular NdFeB magnets according to their microstructural attributes (e.g. inter-grain decoupling, average grain size, and misalignment of easy axes) based on numerical datasets obtained from micromagnetic simulations. We con

SUBMITTER: Park HK 

PROVIDER: S-EPMC7884417 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature