### Abstract: Advancing Electrochemical Urea Synthesis Through Graph Theory and AI Integration Electrocatalysis is a crucial technology for sustainable energy conversion and carbon reduction. Understanding the reaction pathways in electrocatalysis is essential for designing efficient catalysts, but this task is complicated by the dynamic surface effects and the extensive reaction networks involved. To address these challenges, Professor Feng Pan's team at the College of New Materials, Peking University Shenzhen Graduate School, has developed a novel approach that integrates graph theory and structural chemistry. This method, which treats atoms as nodes and chemical bonds as edges in a graph, was initially introduced in 2019 to solve the problem of isomorphism in crystallography (Sci China Chem, 2019, DOI: 10.1007/s11426-019-9502-5). The team subsequently built a big data system containing 650,000 crystal structures and applied this approach to the discovery of low-dimensional materials (National Science Review, 2022, DOI: 10.1093/nsr/nwac028) and the design of new solid-state electrolytes (J. Am. Chem. Soc. 2024, 146, 27, 18535-18543). Recently, the team has extended this methodology to the field of electrochemical catalysis, developing a graph theory-based structural electrochemistry approach. This new method has been particularly effective in elucidating the mechanisms of urea electrocatalytic synthesis, a reaction involving multiple electron transfers and a vast reaction network. The team combined graph theory with an active learning framework powered by machine learning to predict the optimal thermodynamic pathways from a network of hundreds of intermediate species. This approach significantly reduces the computational cost required for exploring reaction pathways, making it a promising tool for the high-throughput design of new catalysts. The research, titled "Automating discovery of electrochemical urea synthesis reaction paths via active learning and graph theory," was published in the prestigious journal *CCS Chemistry* in 2024 (Volume 7, Pages 1-14). The core challenge in catalytic mechanism analysis is identifying the most probable reaction pathways, which is often addressed using quantum chemistry methods like density functional theory (DFT). However, these methods are limited by the complexity of the reaction networks and the computational demands, especially in the context of non-uniform structures and compositions. This limitation is particularly evident in areas such as carbon dioxide electroreduction to multicarbon products and electrochemical C-N coupling, which are currently receiving significant attention. To overcome these limitations, the Pan team introduced a workflow that combines graph theory and an AI active learning loop. Using the example of urea electrocatalytic synthesis, they mapped out most bond rearrangement types and corresponding reconstruction patterns using graph theory. The AI machine learning methods then predicted the stability and formation energy of the reaction network intermediates, thereby reducing the computational cost associated with DFT. The effectiveness of this graph theory-based structural electrochemistry framework was validated using nitrogen-doped graphene as a catalyst, where the reaction system undergoes significant dynamic structural reconstruction under operating conditions. The entire reaction network for urea synthesis includes 901 reaction species. By applying the developed framework, the team was able to evaluate the reaction network energetics and determine the overpotential by computing only 40% of the species. This framework can be extended to other complex electrochemical reactions, facilitating the rapid estimation of overpotentials with minimal reliance on precise quantum chemistry calculations. This advancement paves the way for automated computational analysis of catalytic mechanisms under real-world conditions. ### Key Events: - **Development of Graph Theory-Based Structural Electrochemistry**: The team at Peking University Shenzhen Graduate School integrated graph theory and structural chemistry to create a novel method for analyzing complex reaction networks. - **Validation with Urea Synthesis**: The method was tested and validated using the electrocatalytic synthesis of urea, a reaction that involves significant dynamic structural reconstruction. - **Reduction in Computational Cost**: By using AI and active learning, the team significantly reduced the computational cost required to explore and predict reaction pathways. - **Publication in *CCS Chemistry***: The research findings were published in the Chinese Chemical Society's flagship journal, highlighting the method's potential impact on the field. ### Key People: - **Professor Feng Pan**: Lead researcher and professor at the College of New Materials, Peking University Shenzhen Graduate School. - **Dr. Shunning Li**: Co-corresponding author and associate researcher at the College of New Materials, Peking University Shenzhen Graduate School. - **Shisheng Zheng**: First author, a former doctoral student at Peking University Shenzhen Graduate School and currently a special appointed associate researcher at Xiamen University. ### Key Locations: - **Peking University Shenzhen Graduate School**: The institution where the research was primarily conducted. - **Xiamen University**: The current affiliation of the first author, Shisheng Zheng. ### Time Elements: - **2019**: Initial introduction of the graph theory method in *Sci China Chem*. - **2022**: Application of the method to the discovery of low-dimensional materials in *National Science Review*. - **2024**: Publication of the latest research on electrochemical urea synthesis in *CCS Chemistry*. This innovative approach not only advances the understanding of urea electrocatalytic synthesis but also provides a robust and scalable method for predicting reaction pathways in other complex electrochemical systems, potentially revolutionizing the design and optimization of electrocatalysts.