Abstract:

Theoretical calculation is one of the important means to study the mechanism of catalytic reactions and design high-performance catalysts, which can not only visualize the electronic structure of the active sites and the adsorption configurations of reactants at atomic level, but also can quickly assess catalytic performance by simulating reaction paths, and significantly reduce development cycles and costs. In this paper, we systematically introduced the application of theoretical calculations in the research and development of electrochemical nitrogen reduction of ammonia synthesis (eNRR) with single-atom catalysts (SACs), including the study of the catalytic mechanisms, high-throughput screening of catalysts, and theoretical design of catalysts. The theoretical activities of eNRR single-atom catalysts on different supports were summarized, and the existing problems and development prospects in current research were discussed. Meanwhile multiple strategic approaches, such as optimizing high-throughput calculations, introducing multiscale simulations and advanced quantification methods, and integrating machine learning techniques, were proposed to improve the screening efficiency and performance prediction of electrochemical catalysts, accelerate the precise construction and development of catalysts, and promote the industrialization process of eNRR.

Key words: electrochemical nitrogen reduction to ammonia, single-atom catalysts, computational chemistry, catalyst support, industrialization process

摘要：

理论计算是研究催化反应机理以及设计高性能催化剂的重要手段之一，不仅能从原子层面可视化活性位点的电子结构和反应物吸附构型，还能通过模拟反应路径快速评估其催化性能，显著减少了研发周期与成本。本文系统介绍了理论计算在电化学还原合成氨（eNRR）单原子催化剂（SACs）研发中的应用，包括催化机理研究、催化剂高通量筛选和催化剂理论设计，总结了不同载体上eNRR单原子催化剂的理论活性，并对研究中存在的问题和发展前景进行了展望，提出了优化高通量计算、引入多尺度模拟与高级量化方法、融合机器学习技术等多策略手段，旨在提高电化学催化剂的筛选和性能预测效率，加速催化剂的精准构筑与开发，推动eNRR工业化进程。

关键词: 电化学氮还原合成氨, 单原子催化剂, 计算化学, 催化剂载体, 工业化进程