Data-driven methods, in particular machine learning, can help to speed up the discovery of new materials by finding hidden patterns in existing data and using them to identify promising candidate materials. In the case of superconductors, the use of data science tools is to date slowed down by a lack of accessible data. In this work, we present a new and publicly available superconductivity dataset (‘3DSC’), featuring the critical temperature Tc of superconducting materials additionally to tested non-superconductors.
Deep-read and ML analysis of the Belli-Zurek-Errea 2026 npj Computational Materials paper on bonding descriptors for QNEs in hydride superconductors. Ran Tc, Debye, and DOS predictions on 6 hydride systems (4 SB, 2 AB). ML fails to capture QNE direction; the paper's S_a descriptor fills the gap.
All 10 personalized outreach emails finalized for superconductors (5) and thermoelectrics (5). Each references specific papers and connects to concrete Ouro resources. Blocked on Resend API access.