Liu Yan, Yang Yan, Lin Xuanni, Lin Yutao, Zhuo Zhiwen, Liu Dong, Mao Junjie, Jiang Jun
School of Chemistry and Materials Science, Anhui Normal University, Wuhu, 241002, PR China.
State Key Laboratory of Organic-Inorganic Composites, College of Chemical Engineering, Beijing University of Chemical Technology, Beijing, 100029, PR China.
Nat Commun. 2025 Jun 3;16(1):5158. doi: 10.1038/s41467-025-60170-0.
Diatomic catalysts are promising candidates for heterogeneous catalysis, whereas the rational design meets the challenges of numerous optional elements and the correlated alternation of parameters that affect the performance. Herein, we demonstrate a geometric-electronic coupled design of diatomic catalysts towards oxygen reduction reaction through machine learning derived catalytic "hot spot map". The hot spot map is constructed with two descriptors as axes, including the geometric distance of the diatom and electronic magnetic moment. The narrow hot region in the map indicates the necessary collaborative regulation of the geometric and electronic effects for catalyst design. As a predicted ideal catalyst for oxygen reduction reaction, the N-bridged Co, Mn diatomic catalyst (Co-N-Mn/NC) is experimentally synthesized with a half-wave potential of 0.90 V, together with the embodied zinc air battery displaying high peak power density of 271 mW cm and specific capacity of 806 mAh g . This work presents an advanced prototype for the comprehensive design of catalysts.
双原子催化剂是多相催化中很有前景的候选者,然而合理设计面临着众多可选元素以及影响性能的相关参数变化的挑战。在此,我们通过机器学习衍生的催化“热点图”展示了一种针对氧还原反应的双原子催化剂的几何-电子耦合设计。该热点图以两个描述符为轴构建,包括双原子的几何距离和电子磁矩。图中狭窄的热点区域表明催化剂设计中几何和电子效应需要协同调控。作为预测的氧还原反应理想催化剂,通过实验合成了具有0.90 V半波电位的N桥联Co、Mn双原子催化剂(Co-N-Mn/NC),所展示的锌空气电池具有271 mW cm的高峰值功率密度和806 mAh g的比容量。这项工作为催化剂的综合设计提供了一个先进的原型。