Prediction of magnetic nature of oxide compositions by using machine learning models

The magnetic nature of oxide compositions carries most significance in the applications of science and technology, promoting the development of electronic devices, sensors, and data storage technologies. However, the field encounters a remarkable research gap regarding the use of machine learning techniques for accurately predicting the magnetic behavior of compositions. This study tries to fill the gap by looking into the efficacy of machine learning models for predicting the magnetic nature of oxide compositions, which has been relatively not explored compared to traditional analytical methods. The study seeks to identify the most accurate and reliable machine learning model for this prediction task by evaluating five machine learning models, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). According to this study, the RF outperforms other models on three distinct evaluation metrics, having accuracy 89.53%, precision 92.354%, F1 score 91.343%, while ANN outperforms on the Recall metric having a score 91.43%. This paper closes a significant research gap and improves the applications of materials science and industry by presenting a successful machine learning method that uses Random Forest to predict the magnetic properties of oxide compositions.