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

## TL;DR

This paper uses machine learning to predict the magnetoresistive behavior of a Ni81Fe19 film at high temperatures beyond what was experimentally tested.

## Contribution

The study introduces a machine learning model that accurately predicts magnetoresistivity and estimates the Curie temperature of Ni81Fe19 beyond experimental limits.

## Key findings

- A machine learning model achieved an R2 score of 0.9449 and MSE of 0.0071 in predicting magnetoresistivity.
- The model estimated the Curie temperature of Ni81Fe19 to be approximately 590.97°C.
- Machine learning proved effective in forecasting material properties beyond direct experimental measurements.

## Abstract

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 .