Kolmogorov-Arnold Chemical Reaction Neural Networks for learning pressure-dependent kinetic rate laws
This work addresses the need for interpretable and physics-consistent machine learning models for pressure-dependent kinetics in combustion and chemical systems, representing an incremental improvement over existing CRNNs.
The authors tackled the problem of modeling pressure-dependent reaction kinetics in chemical systems, which standard Chemical Reaction Neural Networks (CRNNs) cannot represent, by developing Kolmogorov-Arnold Chemical Reaction Neural Networks (KA-CRNNs) that accurately reproduce pressure-dependent kinetics, outperforming conventional interpolative models in a proof-of-concept study on the CH3 recombination reaction.
Chemical Reaction Neural Networks (CRNNs) have emerged as an interpretable machine learning framework for discovering reaction kinetics directly from data, while strictly adhering to the Arrhenius and mass action laws. However, standard CRNNs cannot represent pressure-dependent rate behavior, which is critical in many combustion and chemical systems and typically requires empirical formulations such as Troe or PLOG. Here, we develop Kolmogorov-Arnold Chemical Reaction Neural Networks (KA-CRNNs) that generalize CRNNs by modeling each kinetic parameter as a learnable function of system pressure using Kolmogorov-Arnold activations. This structure maintains full interpretability and physical consistency while enabling assumption-free inference of pressure effects directly from data. A proof-of-concept study on the CH3 recombination reaction demonstrates that KA-CRNNs accurately reproduce pressure-dependent kinetics across a range of temperatures and pressures, outperforming conventional interpolative models. The framework establishes a foundation for data-driven discovery of extended kinetic behaviors in complex reacting systems, advancing interpretable and physics-consistent approaches for chemical model inference.