摘要
Multiscale simulation is a key research tool in the quest for new permanent magnets.Starting with first principles methods,a sequence of simulation methods can be applied to calculate the maximum possible coercive field and expected energy density product of a magnet made from a novel magnetic material composition.Iron(Fe)-rich magnetic phases suitable for permanent magnets can be found by means of adaptive genetic algorithms.The intrinsic properties computed by ab initio simulations are used as input for micromagnetic simulations of the hysteresis properties of permanent magnets with a realistic structure.Using machine learning techniques,the magnet’s structure can be optimized so that the upper limits for coercivity and energy density product for a given phase can be estimated.Structure property relations of synthetic permanent magnets were computed for several candidate hard magnetic phases.The following pairs(coercive field(T),energy density product(kJ·m^-3))were obtained for iron-tin-antimony(Fe3Sn0.75Sb0.25):(0.49,290),L10-ordered iron-nickel(L10 FeNi):(1,400),cobalt-iron-tantalum(CoFe6Ta):(0.87,425),and manganese-aluminum(MnAl):(0.53,80).
基金
This work was supported by the EU H2020 project NOVAMAG(686056)and the Austrian Science Fund FWF(I3288-N36).Sergiu Arapan and Pablo Nieves acknowledge the European Regional Development Fund in the IT4Innovations National Supercomputing Center—path to exascale project(CZ 02.1.01/0.0/0.0/16-013/0001791)within the Operational Programme Research,Development and Education,and IT4Innovations computational resources allocated within projects OPEN-11-33,OPEN-14-23,and OPEN-17-14.