Gas–particle partitioning governs the fate of organic molecules and the formation of organic aerosols in the atmosphere. Based on field measurement data, we built machine learning models to predict gas–particle partitioning. We also unveiled previously unrecognized interactions that led to the deviations of partitioning from the equilibrium state under real atmospheric conditions. Our study provided valuable insights for future research in atmospheric chemistry.