  Understanding the correlation between fast and ultrafast demagnetization
processes is crucial for elucidating the microscopic mechanisms underlying
ultrafast demagnetization, which is pivotal for various applications in
spintronics. Initial theoretical models attempted to establish this correlation
but faced challenges due to the complex interplay of physical phenomena. To
address this, we employed a variety of machine learning methods, including
supervised learning regression algorithms and symbolic regression, to analyze
limited experimental data and derive meaningful mathematical expressions
between demagnetization time and the Gilbert damping factor. The results reveal
that polynomial regression and K-nearest neighbors algorithms perform best in
predicting demagnetization time. Additionally,
sure-independence-screening-and-sparsifying-operator (SISSO) as a symbolic
regression method suggested a direct correlation between demagnetization time
and damping factor for Ni and Ni80Fe20, indicating spin-flip scattering
predominantly influences the ultrafast demagnetization 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 ultrafast
demagnetization behavior. This study underscores the potential of machine
learning in unraveling complex physical phenomena and offers valuable insights
for future research in ultrafast magnetism.
