Cracking the challenge of synthesizing ammonia under mild conditions: Machine learning guided plasma electrocatalytic synergistic mechanism and rational design

The system explores the key challenges and cutting-edge solutions faced in achieving efficient synthesis of ammonia under mild conditions. The traditional catalytic Haber method is limited by thermodynamic equilibrium and harsh reaction conditions, while the emerging synergistic pathway of plasma activation and electrochemical nitrogen reduction reaction shows breakthrough potential. This pathway effectively dissociates the strong triple bonds of nitrogen through plasma activation and combines with electrocatalytic processes for efficient protonation at room temperature and pressure, thereby significantly reducing the reaction energy barrier. However, the complexity of this multiphase catalytic system poses higher requirements for understanding the reaction mechanism and optimizing catalyst design. In view of this, the guiding role of machine learning technology in this field is emphasized: by integrating multi-scale simulations and high-throughput experimental data, machine learning can deeply analyze the complex dynamic processes on the plasma catalytic interface, accurately describe the adsorption behavior of key intermediate species, and establish the structure-activity relationship between material descriptors and catalytic performance, ultimately achieving rational design of nitrogen reduction reaction electrocatalysts and composite system configurations, providing theoretical basis and innovative paradigm for accelerating the development of low-energy ammonia synthesis technology.