Crystal twins: self-supervised learning for crystalline material property prediction

Abstract

Machine learning (ML) models have been widely successful in the prediction of material properties. However, large labeled datasets required for training accurate ML models are elusive and computationally expensive to generate. Recent advances in Self-Supervised Learning (SSL) frameworks capable of training ML models on unlabeled data mitigate this problem and demonstrate superior performance in computer vision and natural language processing. Drawing inspiration from the developments in SSL, we introduce Crystal Twins (CT): a generic SSL method for crystalline materials property prediction that can leverage large unlabeled datasets. CT adapts a twin Graph Neural Network (GNN) and learns representations by forcing graph latent embeddings of augmented instances obtained from the same crystalline system to be similar. We implement Barlow Twins and SimSiam frameworks in CT. By sharing the pre-trained weights when fine-tuning the GNN for downstream tasks, we significantly improve the performance of GNN on 14 challenging material property prediction benchmarks.