Autonomous synthesis system integrating theoretical, informatics, and experimental approaches for large-magnetic-anisotropy materials. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Furuya, Daigo; Miyashita, Takuya; Miura, Yoshio; Iwasaki, Yuma; Kotsugi, Masato
• Is Part Of: Science and Technology of Advanced Materials: Methods. Volume 2:Issue 1(2022)
• Page Start: 280
• Page End: 293
• Publication Date: 2022-12-31
• Subjects: Ferromagnetic materials -- magnetocrystalline anisotropy -- Bayesian optimisation -- machine learning -- L10-type ferromagnet -- thin films -- multilayer -- superconducting quantum interference device (SQUID) -- electronic structure
• DOI: 10.1080/27660400.2022.2094698
• Languages: English
• ISSNs: 2766-0400

Abstract

ABSTRACT: We developed an autonomous and efficient system for synthesising ferromagnetic materials with large magnetocrystalline anisotropy by integrating theoretical, informatics, and experimental approaches. By combining the first-principles calculation of the magnetic anisotropy with Bayesian optimisation, we virtually screened candidate materials, comprising four elements and four-layer periods, from various magnetic multilayers. We employed the expected improvement as the acquisition function and Matern52 as the kernel function, to develop a robust machine learning model. We fabricated the top three predicted magnetic materials under laboratory conditions by monoatomic layer deposition and evaluated their magnetic anisotropy using a superconducting quantum interference device (SQUID). Ultimately, we demonstrated that [Fe/Co/Fe/Ni]13 is a novel ferromagnetic material whose magnetic anisotropy exceeds that of L 10 -FeNi- and L 10 -FeCo-type alloys. Furthermore, the origin of the perpendicular magnetic anisotropy was derived from the spin-conserving as well as the spin-flip terms. We determined that Bayesian optimisation offers promising configurability features in terms of the electronic structure that extend beyond the empirical knowledge and human intuition. Graphical abstract: uf0001