Discovery of interpretable <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>T</mml:mi> <mml:mi>c</mml:mi> </mml:msub> </mml:math> descriptors in conventional superconductors guided by symbolic regression

A century after the discovery of superconductivity, the search for new superconductors still relies largely on trial and error, challenging researchers to identify the universal design principles that govern why certain materials superconduct at higher temperatures. Here, the authors introduce an interpretable, data-driven approach that pairs Random Forest screening with SISSO symbolic regression to reveal fundamental ``material genes'' governing Tc in conventional BCS superconductors. Unlike black-box predictors, this study reveals physically meaningful relationships that provide actionable guideline for high Tc: a near half-filled d-orbital per atom combined with moderate heterogeneity in unfilled orbitals.