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The electrochemical CO2 reduction reaction (ECO2RR) driven by renewable electricity holds promise to store intermittent energy in chemical bonds, while producing value-added chemicals and fuels sustainably. Unfortunately, it remains a grand challenge to simultaneously achieve a high faradaic efficiency (FE), a low overpotential, and a high current density of the ECO2RR. Herein, we report the synthesis of heterostructured Bi–Cu2S nanocrystals via a one-pot solution-phase method. The epitaxial growth of Cu2S on Bi leads to abundant interfacial sites and the resultant heterostructured Bi–Cu2S nanocrystals enable highly efficient ECO2RR with a largely reduced overpotential (240 mV lower than that of Bi), a near-unity FE (>98%) for formate production, and a high partial current density (2.4- and 5.2-fold higher JHCOO− than Cu2S and Bi at −1.0 V vs. reversible hydrogen electrode, RHE). Density functional theory (DFT) calculations show that the electron transfer from Bi to Cu2S at the interface leads to the preferential stabilization of the formate-evolution intermediate (*OCHO). 

Abstract The electrochemical nitrate reduction reaction (NO₃RR) to ammonia is an essential step toward restoring the globally disrupted nitrogen cycle. In search of highly efficient electrocatalysts, tailoring catalytic sites with ligand and strain effects in random alloys is a common approach but remains limited due to the ubiquitous energy-scaling relations. With interpretable machine learning, we unravel a mechanism of breaking adsorption-energy scaling relations through the site-specific Pauli repulsion interactions of the metald-states with adsorbate frontier orbitals. The non-scaling behavior can be realized on (100)-type sites of ordered B2 intermetallics, in which the orbital overlap between the hollow *N and subsurface metal atoms is significant while the bridge-bidentate *NO₃is not directly affected. Among those intermetallics predicted, we synthesize monodisperse ordered B2 CuPd nanocubes that demonstrate high performance for NO₃RR to ammonia with a Faradaic efficiency of 92.5% at −0.5 V_RHEand a yield rate of 6.25 mol h⁻¹g⁻¹at −0.6 V_RHE. This study provides machine-learned design rules besides thed-band center metrics, paving the path toward data-driven discovery of catalytic materials beyond linear scaling limitations.