Applying Machine Learning to Elucidate Ultrafast Demagnetization Dynamics in Ni and Ni₈₀Fe₂₀

Understanding the correlation between fast and ultrafast demagnetization (UFD) processes is crucial for elucidating the microscopic mechanisms underlying UFD, which is pivotal for various applications in spintronics. Initial theoretical models attempt to establish this correlation but face challenges due to the complex interplay of physical phenomena. To address this, a variety of machine learning (ML) methods are employed, including supervised learning regression algorithms and symbolic regression (SR), to analyze limited experimental data and derive meaningful mathematical expressions between demagnetization time ( τ M ) and the Gilbert damping factor ( α ). The results reveal that polynomial regression and K‐nearest neighbors algorithms perform best in predicting τ M . Additionally, variable‐selection sure‐independence‐screening‐and‐sparsifying‐operator (VS‐SISSO) as a SR method suggests a direct correlation between τ M and α for Ni and Ni 80 Fe 20 , indicating spin‐flip scattering predominantly influences the UFD mechanism. The developed models demonstrate promising predictive capabilities, validated against independent experimental data. Comparative analysis between different materials underscores the significant impact of material properties on UFD behavior. This study underscores the potential of ML in unraveling complex physical phenomena and offers valuable insights for future research in ultrafast magnetism.