Crystal Structure Based Graph Neural Network And LLM Multi-Modal Approach for Material Magnetic Ordering Prediction

Abstract

AI/ML models are useful for the prediction of materials' properties. Recently, there has been great progress in developing Neural Network models to predict the properties of solids, molecules, and crystals, avoiding expensive and time-consuming laboratory testing. This study presents a novel AI/ML-based multi-modal approach to predict material magnetic ordering using material crystal structure information as input to a Graph Neural Network model (GNN) and semantic text descriptions of the material's properties and structure as input to a Large Language Model (LLM), before performing information fusion of the GNN and LLM outputs to predict material magnetic ordering in four classes (Non-magnetic, Ferromagnetic, Antiferromagnetic, Ferrimagnetic). The model is trained, validated, and tested on a dataset which is a combination of Materials Project dataset, Magndata dataset, and a set of uranium compounds with magnetic ordering determined in laboratory testing, with an 80/10/10 (training/validation/testing) data split. The proposed GNN and LLM multi-modal approach achieves better overall accuracy than both the standalone GNN and LLM approaches with three fusion strategies.