Unravelling the origin of bifunctional OER/ORR activity for single-atom catalysts supported on C2N by DFT and machine learning,Journal of Materials Chemistry A

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Unravelling the origin of bifunctional OER/ORR activity for single-atom catalysts supported on C2N by DFT and machine learning
Journal of Materials Chemistry A ( IF 10.7 ) Pub Date : 2021-07-05 , DOI: 10.1039/d1ta04256d
Yiran Ying _{1,

2,

3,

4} , Ke Fan _{1,

2,

3,

4} , Xin Luo _{5,

6,

7,

8,

9} , Jinli Qiao _{4,

10,

11,

12,

13} , Haitao Huang _{1,

2,

3,

4}

Affiliation

Designing high-performance bifunctional oxygen evolution/reduction reaction (OER/ORR) catalysts is a newly emerging topic and these catalysts have wide applications in metal–air batteries and fuel cells. Herein, we report a group of (27) single-atom catalysts (SACs) supported on the C₂N monolayer as promising bifunctional OER/ORR catalysts by theoretical calculations. In particular, Rh@C₂N exhibits a lower OER overpotential (0.37 V) than the IrO₂(110) benchmark with good ORR activity, while Au and Pd@C₂N are superior ORR catalysts (with an overpotential of 0.38 and 0.40 V) to Pt(111) and their OER performance is also outstanding. More importantly, we discover the origin of the bifunctional catalytic activity by density functional theory (DFT) calculations and machine learning (ML). Using DFT, we find a volcano-shaped relationship between the catalytic activity and ΔG_O, and finally link them to the normalized Fermi abundance, a parameter based on the electronic structure analysis. We further unravel the origin of element-specific activity by ML modelling based on the random forest algorithm that considers the outer electron number and oxide formation enthalpy as the two most important factors, and our model can give an accurate prediction of ΔG_O with much reduced time and cost. This work not only paves the way for understanding the origin of bifunctional OER/ORR activity of SACs, but also benefits the rational design of novel SACs for other catalytic reactions by combining DFT and ML.

中文翻译：

通过 DFT 和机器学习揭示 C2N 上支持的单原子催化剂的双功能 OER/ORR 活性的起源

设计高性能双功能析氧/还原反应（OER/ORR）催化剂是一个新兴课题，这些催化剂在金属-空气电池和燃料电池中有着广泛的应用。在此，我们通过理论计算报告了一组 (27) 单原子催化剂 (SAC) 负载在 C ₂ N 单层上作为有前途的双功能 OER/ORR 催化剂。特别是，Rh@C ₂ N 表现出比具有良好 ORR 活性的 IrO ₂ (110) 基准更低的 OER 过电位 (0.37 V) ，而 Au 和 Pd@C ₂N 是优于 Pt(111) 的 ORR 催化剂（具有 0.38 和 0.40 V 的过电位），并且它们的 OER 性能也很出色。更重要的是，我们通过密度泛函理论（DFT）计算和机器学习（ML）发现了双功能催化活性的起源。使用DFT，我们发现催化剂的活性和Δ之间的火山形关系ģ _Ò，最后它们链接到归一化费米丰度，基于电子结构分析的参数。我们通过基于随机森林算法的 ML 建模进一步揭示了元素特异性活动的起源，该算法将外电子数和氧化物形成焓视为两个最重要的因素，我们的模型可以准确预测 Δ G _O大大减少了时间和成本。这项工作不仅为理解 SAC 的双功能 OER/ORR 活性的起源铺平了道路，而且通过结合 DFT 和 ML，有利于合理设计用于其他催化反应的新型 SAC。

更新日期：2021-07-28

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