Probing the microscopic origin of magnetism in two-dimensional materials using machine learning

Magnetic ordering in two-dimensions is at the forefront of research since the discovery of magnetism in monolayer CrI₃ in 2017. We study two-dimensional (2D) materials with intrinsic magnetic order and explore the microscopic origins of magnetism in these novel materials. The Mermin-Wagner theorem asserts that magnetic ordering cannot occur in 2D without the presence of magnetocrystalline anisotropy (MCA), which arises due to spin-orbit coupling. 2D materials with magnetic order provide a platform with which to study magnetism in low dimensions – the positions of all the atoms are known in theoretical studies and some experimental studies. This contrasts studies of magnetism in thin films. We use data analytics to study the magnetic and thermodynamic properties of 2D materials. Crystal structures based on monolayer CrI₃, are studied using density functional theory (DFT) calculations and machine learning. Magnetic properties, such as MCA and the magnetic moment are determined. We show that machine learning, combined with DFT, provides a means to learn patterns in 2D magnetic materials data, thereby providing insights into the microscopic origins of magnetic ordering in 2D. This approach to materials research also facilitates the rapid discovery of 2D magnets.