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

E. Dengina, A. Bolyachkin, H. Sepehri-Amin, K. Hono

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Article number114797
JournalScripta Materialia
Publication statusPublished - 2022 Sept
Externally publishedYes


  • FePt
  • heat-assisted magnetic recording
  • machine learning
  • micromagnetic simulation
  • neural network

ASJC Scopus subject areas

  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering
  • Metals and Alloys


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