Advanced Machine Learning Models for High-Temperature Magnetoresistivity Predictions of Ni

A 5 nm thick polycrystalline Ni81Fe19 film was sputter-deposited onto a circular 3-inch diameter, 390 μm thick single-crystal wafer with SiO2 surface layers. The magnetoresistance (MR) of the sample was analyzed as a function of applied DC magnetic field and temperature using the Van der Pauw technique. Magnetic measurements were carried out over a temperature range of 25 °C to 350 °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 °C; the best-performing model achieved R2 values up to 0.9449 with MSE as low as 0.0071, and enabled Curie temperature estimation with TC≈590.97 °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 Ni81Fe19 .