Artificial intelligence guided studies of two-dimensional Ising ferromagnets

The discovery of van der Waals (vdW) materials with intrinsic magnetic order in 2017 has given rise to new avenues for the study of emergent phenomena in two dimensions. In particular, a monolayer of CrI₃ was found to be an Ising ferromagnet. Other vdW transition metal halides, such as CrBr₃, were later found to have different magnetic properties. How many vdW magnetic materials exist in nature? What are their magnetic properties? How do these properties change with the number of layers? A conservative estimate for the number of candidate vdW materials (including monolayers, bilayers and trilayers) exceeds ~10⁶. A recent study showed that machine learning can be exploited to discover new vdW Heisenberg ferromagnets based on Cr₂Ge₂Te₆ [1]. In this talk, we will use materials informatics – materials science combined with artificial intelligence (AI) ­– as a tool to efficiently explore the large chemical space of vdW transition metal halides and to guide the discovery of magnetic vdW materials with desirable spin properties. That is, we investigate crystal structures based on monolayer Cr₂I₆ of the form A₂X₆, which are studied using density functional theory (DFT) calculations and AI. Magnetic properties, such as the magnetic moment are determined. The formation energy is also calculated and used as a proxy for the chemical stability. We show that AI, combined with DFT, can provide a computationally efficient means to predict properties of vdW magnets. In addition, data analytics provides insights into the microscopic origins of magnetic ordering in two dimensions. We also explore how our study of magnetic monolayers can be extended, with proper modification, to multilayer vdW materials. This non-traditional approach to materials research paves the way for the rapid discovery of chemically stable magnetic vdW materials with potential applications in spintronics and data storage.