Classifying Magnetic and Non-magnetic Two-dimensional Materials by Machine Learning

Abstract

There has been a giant leap in technological advancement with the introduction of graphene and its remarkable properties after 2005. Since the inception of graphene, the new class of materials called 2D materials are actively being focused on for their potential use case. The recent introduction of magnetism in 2D materials has sparked a new interest among researchers due to the potential use of magnetic properties in spintronics, which is highly admired in storage devices. The extensive library of newly predicted or even synthesized 2D materials made it impossible to screen them experimentally. Therefore, theoretical and computational tools like Density Functional Theory (DFT), Monte Carlo and Molecular dynamics simulations have been the tool of choice for high-throughput screening and insight finding. Even though computational methods worked well, but they generally demand substantial computational resources. The expanding grasp of machine learning algorithms has been overreaching for material engineering. The idea to club ML algorithms with the rising 2D crystal structures and their DFT calculated properties along with other material data has enabled us to create predictive models encompassing underlying physics using machine learning which can screen the materials much faster with relatively similar accuracies in limited resources. Many materials have been investigated using machine learning algorithms to predict their properties, such as crystal structures, curie temperatures, bandgaps, Fermi energies, and charge density wave phases.

In this work, we use a graph-based neural network model (CGCNN) and several highly customized hybrid ML models to identify the magnetic materials from three different databases with heavily skewed data topology. We have employed several supervised ML algorithms to determine how accurate they are in predicting the magnetic state or the amount of anisotropy using the crystal structure as the only source of information. A further effort to develop a complementary regression model for the prediction of magnetic anisotropy