Electrocatalysis has emerged as one of the most promising alternatives to conventional anthraquinone for preparing hydrogen peroxide (H₂O₂) with high energy consumption and pollution because of its simplicity, convenience, and environmental friendliness. However, the oxygen reduction reaction (ORR) generating H₂O₂ via the 2e^- path is a competitive path for 4e^–ORR to generate H₂O. Therefore, it is crucial to identify an electrocatalyst with high selectivity and activity of 2e^-ORR. Here, we established five machine learning (ML) models based on the adsorption free energy of O* (△G (O*)) of 149 single-atom catalysts (SACs) collected and the limiting potential (U_L) of 31 SACs calculated using density functional theory (DFT) from the literature. We then obtained descriptors that could accurately describe SACs. Furthermore, 690 unknown SACs’ 2e^-ORR catalytic performance was well predicted. Four 2e^-ORR materials with high selectivity and activity were screened: Zn@Pc-N₃C₁, Au@Pd-N₄, Au@Pd-N₁C₃, and Au@Py-N₃C₁. We verified the U_L of these SACs through DFT calculation, which was higher than the standard value, proving the ML model’s validity. The ML-based method to predict the material properties with highly selective and active electrocatalysts provides an efficient, rapid, and low-cost method for discovering and designing more valuable SACs catalysts.

电催化因其简单、方便和环境友好等优点，已成为传统蒽醌制备过氧化氢（H ₂ O ₂）最有前途的替代方法之一，具有高能耗和高污染的特点。然而，通过2e ^-路径生成H ₂ O ₂ 的氧还原反应（ORR）是4e ^- ORR生成H _{2 O的竞争路径。因此，寻找具有2e} ^-高选择性和活性的电催化剂至关重要。   ORR。在这里，我们基于收集的 149 个单原子催化剂 (SAC) 的 O* (△G (O*)) 吸附自由能和计算的 31 个 SAC 的极限电位 (U _L ) 建立了五个机器学习 (ML) 模型使用文献中的密度泛函理论 (DFT)。然后我们获得了可以准确描述 SAC 的描述符。此外，还很好地预测了 690 个未知 SAC 的 2e ^- ORR 催化性能。筛选出四种具有高选择性和活性的2e ^{- ORR材料：Zn@Pc-N} ₃ C ₁、Au@Pd-N ₄、Au@Pd-N ₁ C ₃和Au@Py-N ₃ C ₁。我们验证了 U _L通过 DFT 计算得出的这些 SAC 高于标准值，证明了 ML 模型的有效性。基于 ML 的方法用高选择性和活性电催化剂预测材料特性，为发现和设计更有价值的 SAC 催化剂提供了一种高效、快速和低成本的方法。