Predicting Superconductor Critical Temperatures with Graph Convolutional Network

Material property prediction, essential in AI-driven science, faces complexity due to the intricate influence of elemental combinations on superconducting characteristics. Acknowledging that the elemental composition and interactions within material molecules naturally align with graphical representation, we herein apply a graph neural network (GNN)-based methodology. By integrating molecular features alongside chemical descriptors, our approach comprehensively captures elemental synergy. Upon evaluation with the SuperCon dataset, our model showcases a significant leap in prediction accuracy, demonstrating its proficiency in estimating critical temperatures of superconductors. This breakthrough notably propels guided material synthesis and deepens the comprehension of superconductivity, thereby accelerating progress in the development of high-temperature superconductors.