CrysMMNet: Multimodal Representation for Crystal Property Prediction
Kishalay Das Pawan Goyal Seung-Cheol Lee Satadeep Bhattacharjee Niloy Ganguly
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 and 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 two 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 that
fusing the textual representation with crystal graph structure provides
consistent improvement for all the SOTA GNN models compared to their own
vanilla versions. We have shared the textual dataset, that we have curated for
both the benchmark material databases, with the community for future use.