In-silico discovery of HER/OER multi-metallic Alloy electrocatalysts through Density Functional theory calculations and active learning and machine learning

Multi-metallic alloy catalysts have gained attention as means of finding better alternatives to the currently known pure and bimetallic catalysts. As multi-metallic alloys possess a diverse set of surface sites, tuning the distribution of active adsorption and reaction site is key in changing the catalytic performance. However, the combinatorial search of the multi-elemental space is expensive, with the different elemental selection and ratio of mixing being the cause.

Herein, we propose different machine learning frameworks for the efficient search of promising multi-metallic alloys in hydrogen evolution reaction (HER) and oxygen evolution reactions (OER), the two reactions needed to produce green hydrogen. In the first part, Active Learning algorithm is implemented, showing that when adequately searched, the adsorption energy, and consequently the activity of the multi-metallic catalyst can be predicted to within MAE accuracy of 0.1 eV, with only a small fraction of possible DFT calculation candidates. In the second part, the expansion of search space in alloy species is pursued through the utilization of the electronic density of states (DOS) information of catalyst atoms, in conjunction with graph neural networks for the structural information of the alloy surfaces. We show generalizability of the machine can be achieved through multi-task learning approach of DOS and adsorption energy prediction tasks.