报告人：Atsufumi.Hirohata

报告题目：New Ferromagnetic Materials for SpintronicDevices Predicted by Machine Learning

报告时间：2024年7月17日上午9：30-10：30

报告地点：实验四号楼202

主办单位：广东工业大学物理与光电工程学院

报告人简介

Atsufumi.Hirohata（廣畑 貴文，ヒロハタ アツフミ），東北大学 先端スピントロニクス研究開発センター教授：

1 Center for Science and Innovation in Spintronics, Tohoku University, Sendai 980-8577,Japan.

2 Research Institute of Electrical Communication,Tohoku University, Sendai 980-8577,Japan

3 Max Planck Institute for Chemical Physics of Solids, Dresden 01187, Germany.

Atsufumi Hirohata joined the School of Physics, Engineering and Technology inSeptember 2007. He has over 15 years of experience in spintronics, ranging frommagnetic-domain imaging to spin-current interference. He is currently an editorialboard member of Journal of Physics D and Spin. He is also a member of bothAdministrative and Techical Committees of the IEEE Magnetics Society. He holds avisiting associate professorship at Tohoku University and a Royal Society IndustryFellowship in collaboration with Hitachi Cambridge Laboratory.

报告内容

In spintronics, magnetic tunnel and giant magnetoresistive junctions havebeen commonly used for magnetic recording, memories and sensors [1,2]. Thesejunctions typically consists of a CoFeB/MgO/CoFeB trilayer. They satisfy theendurance required for fabrication and operation. For further improvement intheir performance, namely their magnetoresistance ratios, Heusler alloys can bean ideal candidate due to their half-metallicity.

In this study, machine learning was used for the search of new Heusler alloys tosatisfy the above requirements with maintaining the 100% spin polarisation attheir Fermi level. As an example, a CoIrMnAl alloy was predicted to beferromagnetic in experimental and theoretical studies [3,4]. The films weresputtered using ultrahigh vacuum magnetron sputtering on MgO(001) and Sisubstrates. The structural and magnetic characterisation was done by X-raydiffraction and transmission electron microscopy, and vibrating samplemagnetometry, respectively.The optimised films were implemented in a magnetictunnel junction for transport measurements, showing over 100% tunnellingmagnetoresistance ratioThe material search is found to be useful by combiningwith ab initio calculations on alloys suggested by machine learning.

This work was partially supported by JST-CREST (No.JPMJCR17J5) andEPSRC(EP/V007211/1).

References

[1] A.Hirohata et al., J.Magn.Magn.Mater.509,166711(2020).

[2] A.Hirohata et al., Front. Phys. 10, 1007989 (2022).

[3] T.Roy et al.,J.Magn.Magn.Mater.498,166092(2020).

[4] R.Monma et al.,J.Alloys Comp.868,159175(2021).