摘要
Magnetoelastic couplings in giant magnetostrictive materials(GMMs)attract significant interests due to their extensive applications in the fields of spintronics and energy harvesting devices.Understanding the role of the selection of materials and the response to external fields is essential for attaining desired functionality of a GMM.Herein,machine learning(ML)models are conducted to predict saturation magnetostrictions(λ_(s))in RFe_(2)-type(R=rare earth)GMMs with different compositions.According to ML-predicted composition–λsrelations,it is discovered that the values ofλshigher than1100×10^(-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 Tb_(x)Dy_(1-x)Fe_(y).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 Tb_(x)Dy_(1-x)Fe_(y)based PM device through our developed high-throughput(HTP)micromagnetic simulation(MMS)algorithm.Accordingly,high sensitivities up to10.22-13.61 m T·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 highperformance magnetostrictive materials and PM sensing devices.
出处
《Rare Metals》
SCIE
EI
CAS
CSCD
2024年第5期2251-2262,共12页
稀有金属（英文版）
基金
financially supported by the National Key R&D Program of China(No.2021YFB3501401)
the National Natural Science Foundation of China(Nos.52001103,U22A20117)
Zhejiang Provincial Natural Science Foundation of China(No.LQ21E010001)。
