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.

Heteroatom doped atomically dispersed Fe 1 -NC catalysts have been found to show excellent activity toward oxygen reduction reaction (ORR). However,t he origin of the enhanced activity is still controversial because the structurefunction relationship governing the enhancement remains elusive.Herein, sulfur(S)-doped Fe 1 -NC catalyst was obtained as amodel, which displays asuperior activity for ORR towards the traditional Fe-NC materials. 57 Fe Mçssbauer spectroscopy and electron paramagnetic resonance spectroscopyr evealed that incorporation of Si nt he second coordination sphere of Fe 1 -NC can induce the transition of spin polarization configuration. Operando 57 Fe Mçssbauer spectra definitively identified the low spin single-Fe 3+ -atom of C-FeN 4 -S moiety as the active site for ORR. Moreover,DFT calculations unveiled that lower spin state of the Fe center after the Sd oping promotes OH* desorption process.T his work elucidates the underlying mechanisms towards Sd oping for enhancing ORR activity, and paves aw ay to investigate the function of broader heteroatom doped Fe 1 -NC catalysts to offer ageneral guideline for spin-state-determined ORR.

It remains a great challenge to design efficient electrocatalysts for nitrogen reduction reaction (NRR) with high activity and high selectivity. Herein, density functional theory calculations were performed to examine the feasibility of a single transition metal (TM, from Sc to Au) atom supported on a novel graphitic carbon nitride (g-CN) for NRR. It was demonstrated that TM atoms could be anchored on g-CN. With the "acceptance−donation" interaction, the activation of a N 2 molecule was favorably achieved on TM/g-CN. Particularly, five candidates (Nb, Mo, Ta, W, and Re/g-CN) were picked out benefitting from their high NRR activity (limiting potentials of −0.42, −0.39, −0.35, −0.29, and −0.39 V, respectively) and high selectivity (faradic efficiencies of 100, 100, 100, 94, and 69%, respectively). Multiple-level descriptors (ΔG *N , ICOHP, and φ) shed light on the origin of NRR activity from the view of energy, electronic structure, and basic characteristics. The kinetic stability was validated to ensure the feasibility in real experimental conditions. This work broadens the understanding of single-atom catalysts for N 2 fixation and contributes to the discovery of effective NRR electrocatalysts.

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.

Pd/g-C3N4 stands out for the ORR, and multiple-level descriptors involving basic characteristics, electronic structures, charge transfer and energy are established.

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.

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