Machine learning application for functional properties prediction in magnetic materials

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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.

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作者简介

V. Milyutin

Mikheev Institute of Metal Physics, Ural Branch, Russian Academy of Sciences

编辑信件的主要联系方式.
Email: milyutin@imp.uran.ru
俄罗斯联邦, Ekaterinburg

N. Nikul’chenkov

Mikheev Institute of Metal Physics, Ural Branch, Russian Academy of Sciences

Email: milyutin@imp.uran.ru
俄罗斯联邦, Ekaterinburg

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2. Fig. 1. Number of articles in the Web of Science core collection for the query “machine learning + materials science”. A correction factor of 0.8 was used to compensate for search errors.

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3. Fig. 2. Plan of a standard research aimed at developing new materials by developing models to predict their properties.

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4. Fig. 3. Classification of machine learning algorithms for solving regression problems.

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