CO₂ electroreduction is one of the most potential ways to realize CO₂ recycle and energy regeneration. The key to promote this technology is the development of high-performance electrocatalysts. However, the rational design of electrocatalysts with highly catalytic activity and products selectivity toward CO₂ reduction reaction (CO₂RR) remains a challenging task. Herein, we developed a machine learning (ML) model to achieve efficient exploration of electrocatalysts for CO₂RR by combining with density functional theory (DFT). The results show that the electron numbers of the d orbital is the most important descriptor and the support vector regression (SVR) has the best predictive performance. The coefficient of determination (R²) and mean squared error (MSE) are 0.9193 and 0.0162, respectively. Based on the well-trained model, the overpotential of Ni@MoS₂ is successfully predicted to be 0.477 V and it shows the best electrocatalytic performance for CO₂RR. DFT calculation results show that *COOH → *CO is the potential-limiting step of CO₂-to-CO electroreduction for Ni@MoS₂. The DFT-calculated overpotential is 0.481 V, which is consistent with the ML-predicted results. This work provides a convenient machine learning model for the effective theoretical design and screening of CO₂-to-CO electrocatalysts.

CO _{2电还原是实现CO 2}循环利用和能量再生的最有潜力的途径之一。推动这项技术的关键是开发高性能电催化剂。然而，合理设计对CO ₂还原反应（CO ₂ RR）具有高催化活性和产物选择性的电催化剂仍然是一项具有挑战性的任务。_{在此，我们开发了一种机器学习 (ML) 模型，以实现对 CO 2}电催化剂的有效探索RR 结合密度泛函理论 (DFT)。结果表明，d轨道的电子数是最重要的描述子，支持向量回归（SVR）的预测性能最好。决定系数 ( R ² ) 和均方误差 (MSE) 分别为 0.9193 和 0.0162。基于经过良好训练的模型，成功预测 Ni@MoS ₂的过电位为 0.477 V，并显示出最佳的 CO ₂ RR 电催化性能。DFT计算结果表明*COOH→*CO是Ni@MoS _{2 CO 2电还原成CO的电位限制步骤}. DFT 计算的过电位为 0.481 V，与 ML 预测的结果一致。_{这项工作为有效的理论设计和筛选CO 2} -to-CO 电催化剂提供了一个方便的机器学习模型。