Unlocking the performance of ternary metal (hydro)oxide amorphous catalysts via data-driven active-site engineering

Ternary metal (hydro)oxide amorphous catalysts are attractive oxygen evolution reaction (OER) catalysts due to their high performance and cost-effectiveness, but a fundamental understanding of their structure–property relationships remains elusive. Herein, we fabricate a highly active ternary metal (hydro)oxide (NiFeCo) OER catalyst, showing an overpotential of 146 mV at 10 mA cm⁻² and ∼300 hours of durability in 1 M KOH. Inspired by this finding, a dataset with first-principles adsorption energies of reaction intermediates at over 300 active-site structures for both oxides and hydroxides is computed and used to train a machine-learning model for screening the dominant factors and unveiling their intrinsic contributions. The computational work confirms that adding Fe and Co makes the original Ni (hydro)oxide reach ultra-low overpotentials below 200 mV through the modulation from hydroxide towards oxide and the formation of active-sites of ternary metallic components. A series of physical properties of the Fe, Co and Ni atoms in the active-sites are further included in the analysis, and it is found that the magnetic moment (mag) plays an important role in the OER activity. This work demonstrates the application of machine-learning methods in atomic-scale active-site engineering to understand the activity mechanism of ternary metal (hydro)oxide amorphous catalysts for water oxidation, and it has the potential to be extended to wider applications.