Carbon-based double-atom/nanocluster electrocatalysts
usually demonstrate
high reactivity toward the oxygen reduction reaction (ORR). However,
experimental screening of optimized double-atom- and nanocluster-based
ORR catalysts is often expensive and time-consuming. In this work,
density functional theory (DFT) calculation is combined with the machine
learning (ML) method to accelerate the screening and prediction of
high-performance double-atom and nanocluster-based ORR catalysts.
A database consisting of 330 ORR intermediate adsorption energies
on 110 catalyst models is constructed by DFT calculations, which allows
a quick ML screening of 1200 candidate ORR catalysts. The reliability
of the ML model is evaluated by the R-square score (R2) and mean absolute
error methods. A set of 25 potential active double-atom and nanocluster-based
ORR catalysts are selected. On the basis of this ML screening, the
binding energy, Bader charge transfer, and ORR reaction kinetics of
the ML-predicted catalysts are considered further. The carbon-based
Fe–Ce double-atom catalyst is predicted to be the best-performing
ORR catalyst in the sample space. The adsorption energy-based DFT–ML
framework provides an attractive approach to accelerate the screening
of efficient double-atom- or cluster-based ORR electrocatalysts.