Physics-residual machine learning predicts oxygen-evolution catalyst activity beyond the training range from sparse polarization measurements

Abstract

Screening oxygen-evolution catalysts on combinatorial libraries requires deciding which candidates receive the remaining measurements. The deciding activity lies beyond each candidate's measured potential window and often above every activity recorded during fitting. We predict it by physics-residual machine learning: the Tafel equation extrapolates the candidate's own measured current and slope, a learned residual attenuated with feature-space distance corrects the magnitude, and an applicability-domain score identifies predictions above the training range before measurement. In a separately fabricated 322-candidate library, 282 above the training maximum, two measurements per candidate gave a mean absolute error of 0.203 mA cm$^{-2}$ against 1.330 for the selected data-driven machine-learning model. Errors inside the training range remained comparable, and 35 labelled catalysts were enough to fit it. In two independent datasets the same construction lowered the overpotential error by 29 to 52%. Campaigns can therefore shorten each measurement and still rank the most active compositions.