The electrochemical CO2 reduction (CO2ER) to multi‐carbon chemical feedstocks over Cu‐based catalysts is of considerable attraction but suffers with the ambiguous nature of active sites, which hinder the rational design of catalysts and large‐scale industrialization. This paper describes a large‐scale simulation to obtain realistic CuZn nanoparticle models and the atom‐level structure of active sites for C2+ products on CuZn catalysts in CO2ER, combining neural network based global optimization and density functional theory calculations. Upon analyzing over 2000 surface sites through high throughput tests based on NN potential, two kinds of active sites are identified, balanced Cu−Zn sites and Zn‐heavy Cu−Zn sites, both facilitating C−C coupling, which are verified by subsequent calculational and experimental investigations. This work provides a paradigm for the design of high‐performance Cu‐based catalysts and may offer a general strategy to identify accurately the atomic structures of active sites in complex catalytic systems.

Copper (Cu) can efficiently catalyze the electrochemical CO2 reduction reaction (CO2RR) to produce value-added fuels and chemicals, among which methane (CH4) has drawn attention due to its high mass energy density. However, the linear scaling relationship between the adsorption energies of *CO and *CH x O on Cu restricts the selectivity toward CH4. Alloying a secondary metal in Cu provides a new freedom to break the linear scaling relationship, thus regulating the product distribution. This paper describes a controllable electrodeposition approach to alloying Cu with oxophilic metal (M) to steer the reaction pathway toward CH4. The optimized La5Cu95 electrocatalyst exhibits a CH4 Faradaic efficiency of 64.5%, with the partial current density of 193.5 mA cm–2. The introduction of oxophilic La could lower the energy barrier for *CO hydrogenation to *CH x O by strengthening the M–O bond, which would also promote the breakage of the C–O bond in *CH3O for the formation of CH4. This work provides a new avenue for the design of Cu-based electrocatalysts to achieve high selectivity in CO2RR through the modulation of the adsorption behaviors of key intermediates.

Developing easily accessible descriptors is crucial but challenging to rationally design single‐atom catalysts (SACs). This paper describes a simple and interpretable activity descriptor, which is easily obtained from the atomic databases. The defined descriptor proves to accelerate high‐throughput screening of more than 700 graphene‐based SACs without computations, universal for 3–5d transition metals and C/N/P/B/O‐based coordination environments. Meanwhile, the analytical formula of this descriptor reveals the structure–activity relationship at the molecular orbital level. Using electrochemical nitrogen reduction as an example, this descriptor's guidance role has been experimentally validated by 13 previous reports as well as our synthesized 4 SACs. Orderly combining machine learning with physical insights, this work provides a new generalized strategy for low‐cost high‐throughput screening while comprehensive understanding the structure‐mechanism‐activity relationship.