Since the seminal works on the application
of density functional
theory and the computational hydrogen electrode to electrochemical
CO2 reduction (eCO2R) and hydrogen evolution
(HER), the modeling of both reactions has quickly evolved for the
last two decades. Formulation of thermodynamic and kinetic linear
scaling relationships for key intermediates on crystalline materials
have led to the definition of activity volcano plots, overpotential
diagrams, and full exploitation of these theoretical outcomes at laboratory
scale. However, recent studies hint at the role of morphological changes
and short-lived intermediates in ruling the catalytic performance
under operating conditions, further raising the bar for the modeling
of electrocatalytic systems. Here, we highlight some novel methodological
approaches employed to address eCO2R and HER reactions.
Moving from the atomic scale to the bulk electrolyte, we first show
how ab initio and machine learning methodologies
can partially reproduce surface reconstruction under operation, thus
identifying active sites and reaction mechanisms if coupled with microkinetic
modeling. Later, we introduce the potential of density functional
theory and machine learning to interpret data from Operando spectroelectrochemical techniques, such as Raman spectroscopy and
extended X-ray absorption fine structure characterization. Next, we
review the role of electrolyte and mass transport effects. Finally,
we suggest further challenges for computational modeling in the near
future as well as our perspective on the directions to follow.