Dynamic embedding representation for graph neural networks to enhance materials property prediction with limited datasets

Abstract Graph neural networks (GNNs) have proven effective in understanding and predicting diverse material properties, even when working with limited datasets. An important step in training GNN is to use an appropriate and informative graph embedding that can adequately represent the structural and compositional information in the chemical space. Current graph embeddings consist of composition and structure-agnostic element-level encodings, which are static in nature. This makes it challenging to differentiate between different compounds on the element level, especially for datasets with limited data size, thereby relying more on the complex input and architecture for model training. Here, we present a novel framework for GNN-based prediction tasks that use dynamic embedding to significantly improve the models’ predictive ability on materials properties with limited data size. We evaluated the proposed framework on multiple materials datasets across various domains to find that the model trained using dynamic embedding outperforms the models trained using conventional static embedding and features obtained using a pre-trained model. The proposed framework holds significant potential for expediting artificial intelligence (AI)-driven materials discovery.