Permutation-invariant deep learning for predicting superconducting critical temperatures from chemical composition

A DeepSet-based regression model is employed to predict the superconducting critical temperature ( T c ) of potential inorganic superconducting compounds. The model is trained on the SuperCon database using a set of 23 descriptors, primarily derived from chemical composition, while explicitly preserving permutation invariance to avoid artefacts arising from arbitrary ordering of elements. Model performance is assessed on the independent Konno COD candidate dataset, as well as on a collection of superconducting compounds reported in recent years. The model exhibits strong predictive accuracy for a substantial fraction of these newly reported materials, demonstrating its ability to generalize beyond the training set. Furthermore, SHAP value analysis is used to interpret the model predictions and to identify the elemental and compositional features that most strongly influence T c . These results underscore the effectiveness of DeepSet architectures in superconductivity-focused materials informatics and highlight the value of explainable machine learning for guiding the discovery of new superconductors.