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

Yong-Woon Kim, Jihyeok Lee, Sungtae Park, Sooseok Choi, Yung-Cheol Byun

Discovery campaigns for oxygen evolution reaction catalysts repeatedly choose, make and measure catalysts. High-throughput platforms stop polarization curves below potentials that damage the catalyst, so the endpoint, the activity at a target potential or current density, often lies beyond the measured window, and the catalysts of most interest are more active than any measured before. Existing methods do not predict these endpoints accurately when few or no endpoints of a new library have been measured. Here we present physics-residual machine learning (PR-ML), which predicts each endpoint as the sum of a Tafel term, computed from the catalyst's own measured curve with an estimated slope, and a residual term learned from labelled catalysts. In twelve Ni-Pd-Pt-Ru thin-film libraries, the current density at 1.70 V$_{RHE}$ was predicted from the currents at 1.40 and 1.55 V$_{RHE}$. Fitted only on earlier libraries, with ridge regression as the residual learner, PR-ML predicted the Ni--Ru library, whose currents mostly exceed theirs, with a mean absolute error of 0.194 mA cm$^{-2}$, against 1.330-1.882 for data-driven models. With five endpoints from the new library and extremely…