Machine Learning Application for Functional Properties Prediction in Magnetic Materials

Machine learning (ML) has proven to be a powerful tool, significantly speeding up and simplifying the development of new materials while enhancing their functional characteristics. In recent years, there has been an exponential growth in the number of scientific publications exploring the use of ML in materials science. Using this approach, various materials, including magnetic ones, are being actively developed and studied. This article aims to critically review research that applies ML to predict the functional characteristics of soft and hard magnetic materials. The paper is divided into three sections: the first outlines the basic principles and algorithms of machine learning, highlighting its use in addressing practical materials science challenges; the second discusses recent advances in developing magnetic functional alloys using ML; the last section provides a critical analysis of the use of machine learning methods in this area, analyzes its advantages and disadvantages, and gives recommendations for organizing such research.