Atomically dispersed M-N-C catalysts are a promising, cost-effective class of materials for reducing CO₂ to value-added products through the CO₂ reduction reaction (CO₂RR). However, complex multi-objective optimization of several properties including catalyst stability, activity, and selectivity for target products are necessary to make CO₂RR more efficient with this class of catalysts. We systematically investigate activity and selectivity for carbon monoxide, formic acid, and hydrogen evolution pathways on model M-N₄C₁₀ active sites for 26 transition metal species. Our work shows that under acidic conditions, all the considered M-N₄C₁₀ sites except M=Fe, Co, Cr, Cd, and Pt should have CO₂RR onset potentials lower than the hydrogen evolution reaction. We identify the transition metal active sites that should catalyze the CO pathway, leading to gaseous CO production, CO poisoning, or reduction to further products. To understand the reasons for predicted activity and selectivity, we furthermore correlate atomic features for the transition metals with the calculated onset potential of each pathway, showing moderate correlation between both electronegativity and atomic radii with the CO₂RR onset potentials. The high-throughput and feature-based approach in this work not only serves as a guide for present experimental efforts but can also serve as a starting point for machine learning efforts to accelerate active site modeling and catalyst discovery.

中文翻译：

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过渡金属-氮-碳材料作为 CO2 还原电催化剂的计算筛选


原子分散的 M-N-C 催化剂是一类前景广阔且经济高效的材料，可通过 CO₂ 还原反应 （CO₂RR） 将 CO₂ 还原为增值产品。然而，要提高此类催化剂的 CO₂RR 效率，必须对多种特性（包括催化剂稳定性、活性和目标产物的选择性）进行复杂的多目标优化。我们系统地研究了 26 种过渡金属物种的模型 M-N₄C₁₀ 活性位点上一氧化碳、甲酸和析氢途径的活性和选择性。我们的工作表明，在酸性条件下，除 M=Fe、Co、Cr、Cd 和 Pt 外，所有考虑的 M-N₄C₁₀ 位点的 CO₂RR 起始电位应低于析氢反应。我们确定了应催化 CO 途径的过渡金属活性位点，从而导致气态 CO 产生、CO 中毒或还原为进一步的产物。为了了解预测活性和选择性的原因，我们进一步将过渡金属的原子特征与计算的每条途径的起始电位相关联，显示电负性和原子半径与 CO₂RR 起始电位之间存在适度相关性。这项工作中的高通量和基于特征的方法不仅可以作为当前实验工作的指南，还可以作为机器学习工作的起点，以加速活性位点建模和催化剂发现。