Machine learning reveals common features of unconventional superconductors with high transition temperatures

Abstract

Superconductors with high critical temperatures that emerges beyond the phonon-mediated regime are usually considered unconventional in nature, yet unlike conventional superconductors, no broadly applicable predictive theory currently guides their discovery. Here, we use interpretable machine learning to uncover a common materials-space signature of high-$T_{\mathrm{c}}$ unconventional superconductors and develop a data-driven strategy for materials discovery. We construct a unified feature representation for each material by integrating compositional statistics, structural information, and latent representations from trained property-prediction models, followed by structure-aware filtering of an experimentally established superconducting dataset. Without using transition-temperature information, unsupervised analysis shows that cuprate and iron-based superconductors occupy a common region of materials space, characterized primarily by large electronegativity deviation and intermediate mean valence-electron number. A supervised $T_{\text{c}}$ model independently identifies the same descriptors as dominant features, providing complementary evidence for their relevance. Using this empirical materials-space prior together with the $T_{\text{c}}$ model, we prioritize candidate materials, recover recently discovered nickelate superconductors, and identify chemically distinct candidates for future investigation.