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

arXiv · AI, language, vision and robotics · article · Sep 20, 2026 · UTC

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

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