  Recent work has shown the potential of graph neural networks to efficiently
predict material properties, enabling high-throughput screening of materials.
Training these models, however, often requires large quantities of labelled
data, obtained via costly methods such as ab initio calculations or
experimental evaluation. By leveraging a series of material-specific
transformations, we introduce CrystalCLR, a framework for constrastive learning
of representations with crystal graph neural networks. With the addition of a
novel loss function, our framework is able to learn representations competitive
with engineered fingerprinting methods. We also demonstrate that via model
finetuning, contrastive pretraining can improve the performance of graph neural
networks for prediction of material properties and significantly outperform
traditional ML models that use engineered fingerprints. Lastly, we observe that
CrystalCLR produces material representations that form clusters by compound
class.
