Ab-initio superfluid weight and superconducting penetration depth
This work creates a fast method using Density Functional Theory (DFT) data to calculate zero-temperature superfluid weight from electronic structure calculations. The method uses kernel regression instead of traditional derivatives to efficiently integrate contributions from band curvature and quantum geometry. This allows for high-throughput screening of superconducting materials by predicting experimental observables like magnetic penetration depth.