Non-noble electrocatalysts discovered by scaling relations of Gibbs-free energies of key oxygen adsorbates in water oxidation

Abstract

Symbolic regression (SR) is the most widely used machine learning (ML) tool for determining the governing equation from a given dataset. However, a major problem associated with SR is gaps in the results (missing results) when more mathematical operations are introduced. We applied deep symbolic regression (DSR) to a dense space of overpotential formulas to reveal the scaling relations of the Gibbs free energies of the key intermediate adsorbates during the oxygen evolution reaction (OER) on FeNi surfaces in alkaline media. The highest-ranked empirical equation f(x) generated from 40 000 000 hidden equations by DSR predicted an optimized electrocatalyst ratio of Fe-8.7 : Ni-91.3, which resulted in a minimum overpotential of 0.368 V in the water-splitting process. Our approach provides a new perspective for understanding nonlinear dynamics in the electrochemical processes of chemical-energy conversion and storage.