Self-supervised Probabilistic Models For Exploring Shape Memory Alloys

This article discusses the development of a self-supervised learning graph neural network model that can extract atomic information from unlabeled crystal structures. By masking atoms of specific elements and using pseudo labels, the model can accurately predict the type of the masked element. This method has potential applications in various fields, including natural language processing and computer vision.