Abstract
Magnetoelastic couplings in giant magne-tostrictive materials(GMMs)attract significant interests due to their extensive applications in the fields of spin-tronics and energy harvesting devices.Understanding the role of the selection of materials and the response to external fields is essential for attaining desired functional-ity of a GMM.Herein,machine learning(ML)models are conducted to predict saturation magnetostrictions(λs)in RFe2-type(R=rare earth)GMMs with different compo-sitions.According to ML-predicted composition-λs rela-tions,it is discovered that the values of λs higher than 1100 × 10-6 are almost situated in the composition space surrounded by 0.26 ≤ x ≤ 0.60 and 1.90 ≤ y ≤ 2.00 for the ternary compounds of TbxDy1-xFey.Assisted by ML predictions,the compositions are further narrowed down to the space surrounded by 0.26 ≤ x ≤ 0.32 and 1.92 ≤ y≤ 1.97 for the excellent piezomagnetic(PM)performance in the TbxDy1-xFey-based PM device through our devel-oped high-throughput(HTP)micromagnetic simulation(MMS)algorithm.Accordingly,high sensitivities up to 10.22-13.61 mT.MPa-1 are observed in the optimized range within which the available experimental data fall well.This work not only provides valuable insights toward understanding the mechanism of magnetoelastic couplings,but also paves the way for designing and optimizing high-performance magnetostrictive materials and PM sensing devices.
基金项目
National Key R&D Program of China(2021YFB3501401)
National Natural Science Foundation of China(52001103)
National Natural Science Foundation of China(U22A20117)
Zhejiang Provincial Natural Science Foundation of China(LQ21E010001)
NETL
NSTL
万方数据