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<p>The traditional approach to analyzing ferromagnetic resonance spectroscopy (FMR) data can produce inconsistent material parameters when measurements are analyzed at broadband and fixed-frequency conditions separately [Nat.&#xa0;Comm.&#xa0;<b>8</b>,&#xa0;234&#xa0;(2017), Figs.&#xa0;4 and&#xa0;5 ]. Machine learning-based global optimization addresses this issue by simultaneously analyzing all FMR data, independent of frequency. Through a comprehensive reanalysis of published data and analysis of independent measurements on epitaxial <InlineEquation ID="IEq3"><EquationSource Format="TEX">\(\text {Co}_{{25}}\text {Fe}_{{75}}\)</EquationSource></InlineEquation> thin films, we demonstrate that this method yields identical magnetic anisotropy parameters at both broadband and fixed-frequency conditions. In contrast, traditional fitting methods produce differences up to 7% when applied to broadband and fixed-frequency measurements separately. This methodology also enables direct extraction of fundamental parameters, such as the <i>g</i>-factor and magnetization, from FMR data alone, with results consistent with independent measurements. By leveraging measurements for all frequencies, the machine learning approach facilitates self-consistent and frequency-independent material evaluation and effectively distinguishes intrinsic properties from measurement artifacts.</p>

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FMR analysis by machine learning leads to remarkable insights into the magnetic anisotropy of \(\text {Co}_{{25}}\text {Fe}_{{75}}\) thin films

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月9日

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  • A. Napierała-Batygolska,
  • A. Krysztofik,
  • P. Tomczak

摘要

The traditional approach to analyzing ferromagnetic resonance spectroscopy (FMR) data can produce inconsistent material parameters when measurements are analyzed at broadband and fixed-frequency conditions separately [Nat. Comm. 8, 234 (2017), Figs. 4 and 5 ]. Machine learning-based global optimization addresses this issue by simultaneously analyzing all FMR data, independent of frequency. Through a comprehensive reanalysis of published data and analysis of independent measurements on epitaxial \(\text {Co}_{{25}}\text {Fe}_{{75}}\) thin films, we demonstrate that this method yields identical magnetic anisotropy parameters at both broadband and fixed-frequency conditions. In contrast, traditional fitting methods produce differences up to 7% when applied to broadband and fixed-frequency measurements separately. This methodology also enables direct extraction of fundamental parameters, such as the g-factor and magnetization, from FMR data alone, with results consistent with independent measurements. By leveraging measurements for all frequencies, the machine learning approach facilitates self-consistent and frequency-independent material evaluation and effectively distinguishes intrinsic properties from measurement artifacts.