Superconductors and Quantum Materials

High-temperature superconductor discovery has been sporadic and largely serendipitous: cuprates in the 1980s, iron-based superconductors in 2008, nickelate superconductors more recently. Each breakthrough opened a new chemical family, but the intervals between breakthroughs span decades. The core difficulty is that the space of crystal structures, compositions, and electronic configurations that could support superconductivity is vast, while the physical intuition guiding researchers is anchored to a small number of known mechanisms. Entire compositional regions that may host novel superconducting phases go unexamined because there is no systematic way to generate plausible candidates outside established families.

Conventional approaches rely on chemical intuition, analogy to known superconductors, or DFT screening of structural databases. Intuition produces incremental modifications (doping variations, pressure studies, isovalent substitutions) that refine existing families but rarely discover new ones. Database screening is restricted to structures already cataloged in repositories like ICSD or the Materials Project, missing synthesizable but unreported compositions. Machine-learning models trained on existing superconductor data suffer from severe class imbalance and do not reliably generalize to compositional regions far from the training set.