Learning to predict superconductivity

Predicting the superconducting transition temperature (T_c) of materials remains a major challenge in condensed matter physics due to the lack of a comprehensive and quantitative theory. We present a data-driven approach that combines chemistry-informed feature extraction with interpretable machine learning to predict T_c and classify superconducting materials. We develop a systematic featurization scheme that integrates structural and elemental information through graphlet histograms and symmetry vectors. Using experimentally validated structural data from the 3DSC database, we construct a curated, featurized dataset and design a new kernel to incorporate histogram features into Gaussian-process (GP) regression and classification. This framework yields an interpretable T_c predictor with an R^2 value of 0.93 and a superconductor classifier with quantified uncertainties. Feature-significance analysis further reveals that GP T_c predictor can achieve near-optimal performance only using four second-order graphlet features. In particular, we discovered a previously overlooked feature of electron affinity difference between neighboring atoms as a universally predictive descriptor. Our graphlet-histogram approach not only highlights bonding-related elemental descriptors as unexpectedly powerful predictors of superconductivity but also provides a broadly applicable framework for predictive modeling of diverse material properties.