The discovery and optimization of electrocatalysts used
in the
electro-reduction reaction of CO2 (CO2RR) to
achieve high activity and selectivity is a costly and time-consuming
process. Due to environmental concerns and the pivotal role of these
catalysts in curbing the escalating consumption of fossil fuels, it
is imperative to explore alternative methods for discovering electrocatalysts
with superior performance in CO2RR. In this context, the
application of machine learning (ML) to a comprehensive data set derived
from experimental articles on electrocatalysts used in CO2RR is proposed, and the most influential parameters of highly promising
catalysts for CO2RR were optimized. The catalyst exhibiting
the highest faradaic efficiency (FE) of 95–100% in electrochemically
producing CO is characterized by the following properties: metal content
ranging from 2.5 to 7.5%, metal-N content ranging from 1.5 to 2.5%,
total N content ranging from 2.0 to 7%, metal–N bond length
ranging from 1.35 to 1.55 Å, free-energy barrier for *COOH ranging
from −0.25 to 0.75 eV, free-energy barrier for *CO ranging
from −1.5 to −0.25 eV, pore size between 7.0 and 15
nm, and a surface area of the carbon support within the range of 350–700
m2/g. The optimal potential is determined between −1.0
and 0.0 V versus a reversible hydrogen electrode, with a predicted
stability of over 80 h. These findings demonstrate the potential of
ML models, especially for a limited amount of experimental data, to
provide desirable predictions for the design of more efficient electrocatalysts
for CO2RR.