The electrochemical reduction of CO₂ and H₂ evolution from water can be used to store renewable energy that is produced intermittently. Scale-up of these reactions requires the discovery of effective electrocatalysts, but the electrocatalyst search space is too large to explore exhaustively. Here we present a theoretical, fully automated screening method that uses a combination of machine learning and optimization to guide density functional theory calculations, which are then used to predict electrocatalyst performance. We demonstrate the feasibility of this method by screening various alloys of 31 different elements, and thereby perform a screening that encompasses 50% of the d-block elements and 33% of the p-block elements. This method has thus far identified 131 candidate surfaces across 54 alloys for CO₂ reduction and 258 surfaces across 102 alloys for H₂ evolution. We use qualitative analyses to prioritize the top candidates for experimental validation.

从水中放出的CO ₂和H ₂的电化学还原可用于存储间歇产生的可再生能源。这些反应的规模扩大需要发现有效的电催化剂，但是电催化剂的搜索空间太大而无法穷举。在这里，我们介绍了一种理论上的全自动筛选方法，该方法结合了机器学习和优化功能来指导密度泛函理论计算，然后将其用于预测电催化剂的性能。我们通过筛选31种不同元素的各种合金来证明该方法的可行性，从而进行涵盖50％d嵌段元素和33％p元素的筛选块元素。迄今为止，该方法已经确定了54种合金的131个候选表面用于CO ₂还原，而102种合金的258个表面用于H ₂析出。我们使用定性分析对实验验证的最佳候选者进行优先级排序。