  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 have mitigated this problem and
demonstrated superior performance in computer vision and natural language
processing tasks. Drawing inspiration from the developments in SSL, we
introduce Crystal Twins (CT): an SSL method for crystalline materials property
prediction. Using a large unlabeled dataset, we pre-train a Graph Neural
Network (GNN) by applying the redundancy reduction principle to the graph
latent embeddings of augmented instances obtained from the same crystalline
system. By sharing the pre-trained weights when fine-tuning the GNN for
regression tasks, we significantly improve the performance for 7 challenging
material property prediction benchmarks
