Magnetic-assisted oxygen evolution reaction via explainable hybrid learning framework

The surface morphology of electrode materials is sensitive to external magnetic fields, which can enhance mass transfer and reduce concentration polarization during the oxygen evolution reaction (OER). However, understanding the scientific mechanism of performance enhancement and determining the optimal cone morphology are challenging tasks. In this study, experiments and numerical simulations in conjunction with the multiphysics coupling, namely electric field, magnetic field, chemical field, and mechanical field, were employed to investigate conical structures and elucidate the effects of magnetic forces on enhancement. Additionally, we compared the enhancements for cones with different surface tip angles and curvatures to optimize morphology. Our explainable learning framework reveals that (1) associative interplay between Lorentz and Kelvin forces: the Lorentz force benefits mass transfer, while the magnetic gradient force hinders it, and (2) a quantitative correlation between surface morphology (tip angle and curvature), force distribution, and mass transfer efficiency. Our investigations reveal the fundamental mechanisms of mass transfer enhancement for conical structure electrodes during OER under a magnetic field, providing insights for further research on magnetic-assisted electrocatalysis.