Advanced Machine Learning Models for High-Temperature Magnetoresistivity Predictions of Ni81Fe19 Monolayers

A \( 5\,nm \) thick polycrystalline \( \mathrm{Ni_{81}Fe_{19}} \) film was sputter-deposited onto a circular 3-inch diameter, \( 390\,\mu m \) thick single-crystal wafer with \( \mathrm{SiO_2} \) surface layers. The magnetoresistance (MR) of the sample was analyzed as a function of the applied DC magnetic field and temperature using the Van der Pauw technique. Magnetic measurements were carried out over a temperature range of \( 25^{\circ}\mathrm{C} \) to \( 350^{\circ}\mathrm{C} \) using a Lake Shore Hall Effect Measurement System (HEMS). An external magnetic field ranging from \( +14$\,kG \) to \( -14$\,kG \) was applied at each temperature value to observe changes in resistance. Hall coefficients and resistance were obtained by applying current in both directions with different contact configurations.Machine learning techniques, including Random Forest Regression, were employed to predict magnetoresistivity beyond \( 350^{\circ}\mathrm{C} \) and estimate the Curie temperature (\( 570^{\circ}\mathrm{C} \)). This study highlights the potential of machine learning in accurately forecasting material properties beyond experimental limits, providing enhanced predictive models for the magnetoresistive behavior and critical temperature transitions of \( \mathrm{Ni_{81}Fe_{19}} \) [1–3].