Keywords: Graph Neural Networks, Multimodal Learning, Crystalline Materials
TL;DR: In this paper, we propose a simple multi-modal framework for crystalline materials, which fuse both graph structural and textual representation together to improve property prediction accuracy.
Abstract: Machine Learning models have emerged as a powerful tool for fast and accurate prediction of different crystalline properties. Exiting state-of-the-art models rely on a single modality of crystal data i.e crystal graph structure, where they construct multi-graph by establishing edges between nearby atoms in 3D space and apply GNN to learn materials representation. Thereby, they encode local chemical semantics around the atoms successfully but fail to capture important global periodic structural information like space group number, crystal symmetry, rotational information etc, which influence different crystal properties. In this work, we leverage textual descriptions of materials to model global structural information into graph structure to learn a more robust and enriched representation of crystalline materials. To this effect, we first curate a textual dataset for crystalline material databases containing descriptions of each material. Further, we propose CrysMMNet, a simple multi- modal framework, which fuses both structural and textual representation together to generate a joint multimodal representation of crystalline materials. We conduct extensive experiments on benchmark datasets across ten different properties to show that CrysMMNet outperforms existing state-of-the-art baseline methods with a good margin. We also observe fusing textual representation with crystal graph structure provides consistent improvement for all the SOTA GNN models compared to their own vanilla version. We have shared the textual dataset, that we have curated for both the benchmark material databases, with the community for future use.
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