Machine learning guided development of high-performance nano-structured nickel electrodes for alkaline water electrolysis

Utilizing a human in the loop Bayesian optimisation paradigm based on Gaussian process regression, we opti-mized an Ni electrodeposition method to synthesize nano-structured, high-performance hydrogen evolution reaction electrodes. Via exploration-exploitation stages, the synthesis process variables current density, tem-perature, ligand concentration and deposition time were optimized influencing the deposition layer morphology and, consequently, hydrogen evolution reaction activity. The resulting structures range from micrometre-sized, star-shaped features to nano-sized sandpaper-like structures with very high specific surface areas and good hydrogen evolution reaction activity. Using the overpotential at 10 mA cm-2 as the figure of merit, hydrogen evolution reaction overpotentials as low as-117 mV were reached, approaching the best known technical high surface area electrodes (e.g. Raney Ni). This is achieved with considerably fewer experiments than what would have been necessary with a linear grid search, as the machine learning model could capture the unintuitive interdependencies of the synthesis variables.