Selective nitrate‐to‐ammonia electrochemical conversion is an efficient pathway to solve the pollution of nitrate and an attractive strategy for low‐temperature ammonia synthesis. However, current studies for nitrate electroreduction (NO3RR) mainly focus on metal‐based catalysts, which remains challenging because of the poor understanding of the catalytic mechanism. Herein, taking single transition metal atom supported on graphitic carbon nitrides (g‐CN) as an example, the NO3RR feasibility of single‐atom catalysts (SACs) is first demonstrated by using density functional theory calculations. The results reveal that highly efficient NO3RR toward NH3 can be achieved on Ti/g‐CN and Zr/g‐CN with low limiting potentials of −0.39 and −0.41 V, respectively. Furthermore, the considerable energy barriers are observed during the formation of byproducts NO2, NO, N2O, and N2 on Ti/g‐CN and Zr/g‐CN, guaranteeing their high selectivity. This work provides a new route for the application of SACs and paves the way to the development of NO3RR.

It is highly desirable to design bifunctional electrocatalysts to realize highly efficient oxygen evolution/reduction reaction (OER/ORR). Herein, density functional theory (DFT) calculations were conducted to validate the feasibility of a single transition metal (TM) embedded in defective g-C 3 N 4 for bifunctional oxygen electrocatalysis. It was clarified that the TM atom supported on defective g-C 3 N 4 with N vacancy (TM/V N -CN) was stable and possible to be synthesized. Remarkably, Rh/V N -CN exhibited low overpotentials of 0.32 and 0.43 V for OER and ORR, respectively, and was considered as the promising bifunctional catalyst. The volcano plots and contour maps were established based on the scaling relation of adsorption energies of *OH, *O, and *OOH. The OER/ORR activity origin was revealed by descriptors of the d-band center and the number of d-orbital electrons multiplied electronegativity of TM. Furthermore, the machine learning (ML) algorithm was utilized to analyze the intrinsic correlation between catalytic activity and a series of structural and atomic features. Our combined DFT and ML work not only opts for the promising bifunctional oxygen electrocatalysts but also provides guidance for the design of single-atom catalysts and the discovery of more efficient catalysts.

Combining NO removal and NH3 synthesis, electrochemical NO reduction reaction (NORR) toward NH3 is considered as a novel and attractive approach. However, exploring suitable catalysts for NO‐to‐NH3 conversion is still a formidable task due to the lack of a feasible method. Herein, utilizing systematic first‐principles calculations, a rational strategy for screening efficient single‐atom catalysts (SACs) for NO‐to‐NH3 conversion is reported. This strategy runs the gamut of stability, NO adsorbability, NORR activity, and NH3 selectivity. Taking transition metal atom embedded in C2N (TM‐C2N) as an example, its validity is demonstrated and Zr‐C2N is selected as a stable NO‐adsorbable NORR catalyst with high NH3 selectivity. Therefore, this work has established a theoretical landscape for screening SACs toward NO‐to‐NH3 conversion, which will contribute to the application of SACs for NORR and other electrochemical reactions.

The highly active and selective carbon dioxide reduction reaction (CO2RR) can generate valuable products such as fuels and chemicals and reduce the emission of greenhouse gases. Single-atom catalysts (SACs) and dual-metal-sites catalysts (DMSCs) with high activity and selectivity are superior electrocatalysts for the CO2RR as they have higher active site utilization and lower cost than traditional noble metals. Herein, we explore a rational and creative density-functional-theory-based, machine-learning-accelerated (DFT-ML) method to investigate the CO2RR catalytic activity of hundreds of transition metal phthalocyanine (Pc) DMSCs. The gradient boosting regression (GBR) algorithm is verified to be the most desirable ML model and is used to construct catalytic activity prediction, with a root-mean-square error of only 0.08 eV. The results of ML prediction demonstrate Ag-MoPc as a promising CO2RR electrocatalyst with the limiting potential of only −0.33 V. The DFT-ML hybrid scheme accelerates the efficiency 6.87 times, while the prediction error is only 0.02 V, and it sheds light on the path to accelerate the rational design of efficient catalysts for energy conversion and conservation.

RuO2 is the most efficient material reported so far for acidic oxygen evolution reaction (OER), yet suffering from insufficient stability in practical water-splitting operations. Targeting on this issue, herein we report an electronic structure modulating strategy by dispersing RuO2 over defective TiO2 enriched with oxygen vacancies (RuO2/D-TiO2). Synergetic (spectro-)­electrochemistry and theoretical simulations reveal a continuous band structure at the interface between RuO2 and defective TiO2, as well as a lowered energetic barrier for *OOH formation, which are accountable for the largely enhanced acidic OER kinetics. As a result, the as-prepared RuO2/D-TiO2 catalyst exhibits a low overpotential of 180 mV at 10 mA cm–2, a low cell voltage of 1.84 V at 2 A cm–2, and a long lifetime above 100 h at 200 mA cm–2, providing hints for a more robust acidic OER catalyst design.