Abstract
Current study results on Machine Learning have been published. According to news reporting originating from Hangzhou, People’s Republic of China, by NewsRx correspondents, research stated, “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.” Funders for this research include Key R&D Program of China, National Key R&D Program of China, National Natural Science Foundation of China (NSFC), Natural Science Foundation of Zhejiang Province. Our news editors obtained a quote from the research from Hangzhou Dianzi University, “Herein, machine learning (ML) models are conducted to predict saturation magnetostrictions (lambda(s)) in RFe2-type (R = rare earth) GMMs with different compositions. According to ML-predicted composition-lambda(s) relations, it is discovered that the values of lambda(s) higher than 1100 x 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 developed high-throughput (HTP) micromagnetic simulation (MMS) algorithm. Accordingly, high sensitivities up to 10.22-13.61 mT ＜middle dot ＞MPa-1 are observed in the optimized range within which the available experimental data fall well.”
NETL
NSTL
Nonferrous Metals Soc China