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The flexible tuning ability of dual-atom catalysts (DACs) makes them an ideal system for a wide range of electrochemical applications. However, the large design space of DACs and the complexity in the binding motif of electrochemical intermediates hinder the efficient determination of DAC combinations for desirable catalytic properties. A crystal graph convolutional neural network (CGCNN) was adopted for DACs to accelerate the high-throughput screening of hydrogen evolution reaction (HER) catalysts. From a pool of 435 dual-atom combinations in N-doped graphene (N6Gr), we screened out two high-performance HER catalysts (AuCo@N6Gr and NiNi@N6Gr) with excellent HER, electronic conductivity, and stability using the combination of CGCNN and density functional theory (DFT). Furthermore, comprehensive DFT studies were conducted on these two catalysts to confirm their outstanding reaction kinetics and to understand the cooperative effect between the metal pair for HER. To obtain ideal hydrogen binding in AuCo, the inert Au weakens the strong hydrogen binding of Co, while for NiNi, the two weakly binding Ni cooperate. The present protocol was able to select the two catalysts with different physical origins for HER and can be applied to other DAC catalysts, which should hasten catalyst discovery.

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Organic cathode materials are plagued by their low cycle stability and poor electronic conductivity, even though they have attracted increasing attention in the context of lithium‐ion batteries (LIBs). Herein, a coordination polymer cobalt‐hexaazatriphenylene hexacarbonitrile (Co(HAT‐CN)) is prepared via a facile solvothermal method, which is composed of the redox‐active HAT‐CN linker and the Co(II) ion center. The fabricated material shows excellent structural stability and high conductivity. Moreover, graphene oxide (GO) is introduced as a substrate, and in‐situ loading of Co(HAT‐CN) on its surface shows enhanced cycling stability. For Co(HAT‐CN)/GO, a high specific capacity of 204 mAh g–1 can be retained even after 200 cycles at a current density of 40 mA g–1 in a voltage window of 1.2–3.9 V. Ex situ and in situ analyses are applied to probe the reversibility of the pyrazine redox‐active center during the cycling process and the lithium storage process. Density functional theory calculations reveal that the high conductivity of Co(HAT‐CN) should be ascribed to the narrow LUMO‐HOMO gap (0.61 eV), and strong binding of lithiated molecules.