AI Insight
The study introduces Kolmogorov-Arnold Chemical Reaction Neural Networks (KA-CRNNs), an extension of existing Chemical Reaction Neural Networks (CRNNs) that incorporates learnable functions to model pressure-dependent and collider-specific kinetic rate behavior directly from data. By using Kolmogorov-Arnold activations to represent kinetic parameters as functions of third-body concentrations, the framework preserves the physical interpretability of Arrhenius and mass action laws while eliminating the need for predefined empirical formulations such as Troe, SRI, PLOG, or Chebyshev polynomials. In two proof-of-concept studies, KA-CRNNs demonstrated accurate reproduction of pressure-dependent kinetics across varying temperatures, pressures, and bath gas mixtures, achieving a 2.88-fold reduction in mean squared error compared to interpolative approaches, even under sparse training data conditions.
Why it matters
Accurate modeling of pressure-dependent reaction kinetics is essential for simulating combustion processes and complex chemical systems, and this framework offers a more flexible and data-driven alternative to manually tuned empirical models. The approach could accelerate the development of reliable chemical mechanisms for applications in engine design, atmospheric chemistry, and industrial chemical processing.
arXiv:2511.07686v2 Announce Type: replace
Abstract: 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 or mixture-based rate behavior, which is critical in many combustion and chemical systems and typically requires empirical falloff formulations such as Troe or SRI, or data-based interpolation or polynomial fits such as PLOG or Chebyshev Polynomials. Here, we develop Kolmogorov-Arnold Chemical Reaction Neural Networks (KA-CRNNs) that generalize CRNNs by modeling each kinetic parameter as a learnable function of third-body concentrations using Kolmogorov-Arnold activations. This structure maintains the Arrhenius and mass action interpretability and physical constraints of a vanilla CRNN while enabling assumption-free inference of global and collider-specific pressure effects directly from data. Two proof-of-concept reaction studies are presented to highlight the capability of KA-CRNNs to accurately reproduce pressure-dependent and collider-specific kinetics across a range of temperatures, pressures, and bath gas mixtures, extracting meaningful and generalizable models from sparse training data and significantly outperforming interpolative approaches (2.88x reduction in MSE). The framework establishes a foundation for data-driven discovery of extended kinetic behaviors in complex reacting systems, advancing interpretable and physics-constrained approaches for chemical model inference.