Bilayer MN4-O-MN4 by bridge-bonded oxygen ligands: Machine learning to accelerate the design of bifunctional electrocatalysts

We designed and screened bifunctional catalysts with good oxygen reduction reaction (ORR) and oxygen evo-lution reaction (OER) performance on bilayer MN4-O-MN4 structures with bridge-bonded oxygen ligands. The ORR and OER catalytic activities of 225 bilayer MN4-O-MN4 structures were explored in an accelerated manner by combining machine learning (ML) and density functional theory (DFT) calculations (DFT-ML). Based on the gradient boosted regression (GBR) algorithm, a series of efficient monofunctional and bifunctional electro-catalysts were successfully predicted with an average prediction error of only 0.04 V and 0.06 V for ORR and OER overpotential (eta). ML successfully predicted that the overpotential of the monofunctional catalysts CoN4-O-RhN4 (ORR) and RhN4-O-AgN4 (OER) reached 0.34 V and 0.29 V, respectively; CoN4-O-AgN4 was considered the best bifunctional catalyst due to its overpotential of eta ORR = 0.35 V and eta OER = 0.33 V on the bifunctional catalysts. Compared with DFT calculations, the DFT-ML accelerated calculation method resulted in a 9.4-fold improvement in catalyst screening speed. The performance prediction of 225 bilayer MN4-O-MN4 structures was used to screen out the potential bifunctional catalysts, thus providing guidance for the experi-mental synthesis of better performing bridge-bonded oxygen ligand catalysts.

Automated summary

In this study, bifunctional catalysts with good performance in oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) were designed and screened using machine learning and density functional theory. A series of efficient monofunctional and bifunctional electro-catalysts were successfully predicted with high accuracy, greatly increasing the speed of catalyst screening.