Integrating machine learning to uncover homogeneous catalytic mechanisms in <i>N</i>‐vinyl‐pyrrolidone synthesis

Abstract Combining machine learning, density functional theory (DFT) calculation, and kinetic modeling offers significant advantages in studying reactions under complex conditions, enabling a detailed exploration of reaction mechanisms. Artificial neural network (ANN) models accurately predict and analyze the impact of reaction conditions, overcoming the limitations of traditional kinetic studies in complex systems. Specifically, the homogeneous catalytic reaction for synthesizing N ‐vinylpyrrolidone from pyrrolidone and acetylene, which has stringent requirements, was selected. The ANN model effectively captured nonlinear relationships between residence time, moisture content, reaction temperature, catalyst concentration, reaction pressure, and catalyst preparation temperature. DFT calculations revealed the reaction pathways, leading to the development of a high‐precision predictive model. This approach not only uncovers potential reaction pathways and intermediates in complex reaction systems but also provides a new avenue for optimizing reaction conditions, resulting in more efficient catalyst design and process development in intricate reaction environments.