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.

Thermal runaway in battery packs is a major safety concern for commercial applications such as electric vehicles, potentially leading to catastrophic outcomes like battery pack fires. This phenomenon occurs due to thermal abuse conditions that lead to exothermic degradation reactions of battery components, such as anode decomposition, cathode conversion, SEI decomposition, and electrolyte breakdown[1, 2]. Typical thermal abuse failure modes include, but are not limited to, physical damage, internal short circuits, overcharging, or overheating (e.g., extreme temperature exposure)[1]. The heat released under such conditions, when a cell or group of cells fails, can lead to a chain reaction where adjacent cells enter a self-heating state and undergo thermal runaway[3]. This propagation can consume an entire battery module or pack. These safety concerns are even more pressing in today's electrification environment, particularly as the industry moves towards higher power and energy density cells[1, 4]. To address these concerns, cell and pack manufacturers must adhere to strict safety protocols to avoid catastrophic outcomes. Simulation-driven design offers a platform to optimize designs and aid in the prevention and mitigation of thermal runaway. For example, thermal analysis of novel heat shield materials can be conducted efficiently to understand their effectiveness at mitigating propagation.

The inference of chemical reaction networks is an important task in understanding the chemical processes in life sciences and environment. Yet, only a few reaction systems are well-understood due to a large number of important reaction pathways involved but still unknown. Revealing unknown reaction pathways is an important task for scientific discovery that takes decades and requires lots of expert knowledge. This work presents a neural network approach for discovering unknown reaction pathways from concentration time series data. The neural network denoted as Chemical Reaction Neural Network (CRNN), is designed to be equivalent to chemical reaction networks by following the fundamental physics laws of the Law of Mass Action and Arrhenius Law. The CRNN is physically interpretable, and its weights correspond to the reaction pathways and rate constants of the chemical reaction network. Then, inferencing the reaction pathways and the rate constants are accomplished by training the equivalent CRNN via stochastic gradient descent. The approach precludes the need for expert knowledge in proposing candidate reactions, such that the inference is autonomous and applicable to new systems for which there is no existing empirical knowledge to propose reaction pathways. The physical interpretability also makes the CRNN not only capable of fitting the data for a given system but also developing knowledge of unknown pathways that could be generalized to similar chemical systems. Finally, the approach is applied to several chemical systems in chemical engineering and biochemistry to demonstrate its robustness and generality.