A provenance-aware zirconia conductivity database and Arrhenius-constrained screening of doped electrolyte candidates

Zirconia solid electrolytes are key oxygen-ion conductors, but reported conductivity values depend strongly on dopant chemistry, processing history and measurement temperature. Here we curate a provenance-aware zirconia conductivity database of 1,351 records and use it to compare an Arrhenius-constrained physics-informed machine-learning model (PIML) with an unconstrained deep neural network (DNN). In a reserved grouped-material confirmation split, the DNN achieves the lower pooled root-mean-square error (RMSE, in log 10 σ units) for withheld composition-processing groups. The Arrhenius-constrained model remains on a comparable error scale and provides bounded effective activation-energy estimates. Its clearest benefit appears in matched-material temperature extrapolation: when trained at ≤ 800 • C and tested at ≥ 900 • C (n = 47), PIML reduces RMSE from 0.4187 to 0.3609 and raises R 2 from 0.8562 to 0.8932. Bootstrap analysis supports this record-level gain, with material-fingerprint resampling reported as a conservative uncertainty check. Genetic-algorithm (GA) screening under fixed processing and measurement conditions identifies a Sc-rich/Mg-containing zirconia region. Exploratory CHGNet molecular dynamics provides the main computational ranking check, with single-configuration oxide-reference density functional theory (DFT) retained as an auxiliary thermodynamic screen. This work provides a curated conductivity resource and a conservative screening route for doped zirconia electrolytes.