Predicting Properties of van der Waals Magnets using Graph Neural Networks

We present a study of two dimensional (2D) magnetic materials using state-of-the-art machine learning models that use a graph-theory framework. Representing materials as graphs allows us to better learn structure-property relationships by leveraging both the chemical properties of the constituent atoms and the connectivity between those atoms. These models are capable of predicting both structure-level (graph-wise) and atom-level (node-wise) features. By simultaneously making predictions on both types of features, we may force our model to learn relationships between local and global properties. This constraint guides the model to more accurately capture the underlying physical interactions. In particular, we train a graph neural network that uses the Atomistic Line Graph Neural Network (ALIGNN) architecture. We train the ALIGNN model on data comprising DFT calculated local and global magnetic moments of 314 2D structures of the form CrAⁱⁱBⁱBⁱⁱX₆ based on monolayer Cr₂Ge₂Te₆. By learning the relationships between local and global magnetic properties, we demonstrate an improvement over models that are only trained on global magnetic properties.