Machine Learning Approach for Evaluation of Nanodefects and Magnetic Anisotropy in FePt Granular Films

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.