npj Computational MaterialsSource publication:
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Computing metallic conductivity from first principles is expensive, so large chemical spaces have gone largely unscreened. This workflow ranks about 2.8 million ordered compounds using two machine-learning proxies, a BoltzTraP2 conductivity under the constant relaxation-time approximation and the Eliashberg electron-phonon coupling λ, combined by symbolic regression into σSR=0.4σBT/λ, then runs full electron-phonon transport on the candidates. Be2IrPt, H3NaNi, LuIr and LiBePt2 reach room-temperature conductivities of roughly 34 to 38×106 S/m, comparable to aluminum's 36.59×106 S/m but still below Ag, Au and Cu.
High-throughput screening of metallic conductivity can now cover about 2.8 million ordered compounds instead of a small set computed one at a time. First-principles transport needs band structures, scattering rates and transport integrals, which confined earlier work to small material sets, and conductivity has remained largely absent from high-throughput databases. Against first-principles validation on about 14,000 compounds, machine-learning predictions show root-mean-square deviations of 4.14×106 S/m for conductivity and 0.18 for λ on overlapping points, and anomalously high false positives were removed by the validation step.
Ranking rests on an empirical expression with two inputs that combines the electronic-structure conductivity with scattering strength. The BoltzTraP2 σBT depends only on the band structure and uses the constant relaxation-time approximation, so it omits electron-phonon scattering and cannot be compared directly with measured conductivity. Symbolic regression on 43 materials with experimental conductivity yields σSR=0.4σBT/λ, which describes noble, alkali and transition metals well, with Sr and Ca the most noticeable outliers.
Top-ranked candidates keep high conductivity after full iterative Boltzmann transport calculations. The screening threshold was σSR>15×106 S/m, and after validation one representative with the highest predicted conductivity was kept per elemental composition, leaving 52 systems. EPW gives Be2IrPt 38.02, H3NaNi 34.82, LuIr 34.41 and LiBePt2 34.35×106 S/m, comparable to aluminum's 36.59×106 S/m, with a mean absolute error of 3.82×106 S/m between σEPW and σSR.
The high conductivity of noble metals comes from a combination of band structure and lattice vibrations rather than one factor. Silver has a closed 4d shell with d-bands about 4–7 eV below the Fermi level, a steeply dispersive 5s band at the Fermi level, low phonon frequencies and very weak coupling to conduction electrons, with λ about 0.1. Aluminum has a multi-sheet Fermi surface and λ about 0.45, superconducts at 1.2 K, and the same coupling appears at room temperature as a higher scattering rate that limits its conductivity relative to silver.
The workflow targets ordered inorganic compounds in the Alexandria database within 25 meV/atom of the convex hull, with transport computed at the scalar-relativistic level and spin-orbit coupling tested only for Pb, Be2IrPt and LiBePt2. The experimental benchmark is a set of elemental metals with established conductivity data, not compositionally complex compounds.
Spin-orbit coupling is neglected, and including it lowers the room-temperature conductivity of Be2IrPt and LiBePt2 by 8.1% and 9.3% respectively while bringing Pb closer to experiment. Ca and Sr show the largest deviations from experiment, which the authors attribute to d-state hybridization near the Fermi level and quasiparticle corrections that may alter Fermi-surface topology. The σBT surrogate is also sensitive to k-point sampling and less accurate than the λ model.