R&D: ML Approach for Evaluation of Nanodefects and Magnetic Anisotropy in FePt Granular Films

Scripta Materialia has published an article written by E.Dengina, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba 305-0047, Japan, and Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba 305-8573, Japan, A.Bolyachkin, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba 305-0047, Japan, and H.Sepehri-Amin, K.Hono, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba 305-0047, Japan, and Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba 305-8573, Japan.

Abstract: “This paper reports a machine learning approach for evaluating micromagnetic and microstructural parameters from demagnetization curves of FePt granular films for heat-assisted magnetic recording (HAMR) media. We developed a neural network to predict parameters of magnetic anisotropy and volume fractions of defects such as [200] misoriented grains, {111} twined variants, and disordered grains. The neural network was trained on a synthetic dataset of out-of-plane demagnetization curves that were simulated using the micromagnetic model constructed from actual nanostructure of a FePt-X HAMR medium. Predicted nanodefects agreed well with those estimated by synchrotron X-ray diffraction, and the demagnetization curve simulated with the predicted parameters accurately reproduced the experimental one. This work paves the way for a high-throughput magnetometry-based characterization of FePt granular media for its structural optimization toward higher areal density of HAMR.“