ML-RKIM: Physics-Informed Machine Learning for Reaction Kinetics Identification and Modeling

Kinetic processes play a central role in structural transformations across natural, engineered, and biological systems, ultimately determining their final properties. Traditional kinetic characterization methods are often time-consuming and resource-intensive, and require prior assumptions about the reaction mechanism. In this work, we present a physics-informed machine learning framework to extract closed-form kinetic representations from time-series data without preselecting a single reaction mechanism. The framework combines neural networks, physics-informed features, and sparse regularization. The framework was applied to a range of processes, including thermally driven reactions such as polymer curing and crystallization, and nonthermal processes such as enzymatic browning. The method identified sparse, closed-form kinetic representations while reducing reliance on a single predefined kinetic model. R2 values exceeded 98% for the synthetic cases and ranged from 76% to 92% for the experimental cases. In addition, a systematic ablation study evaluated robustness to noise, data availability, sampling density, incomplete measurements, and candidate function library variation, and an experimental case study was included to assess the performance under realistic conditions. These results demonstrate the potential of this approach to accelerate kinetic characterization across polymer, food, and biomolecular processing applications, where accurate kinetic models are essential.