Abstract
Model-based analysis is essential for extracting information about chemical reaction kinetics in full detail from time-resolved data sets. This approach combines experimental hypotheses with mathematical and physical models, enabling a concise description of complex system dynamics and the extraction of kinetic parameters like kinetic pathways, time constants, and species amplitudes. However, building the final kinetic model requires several intermediate steps, including testing various assumptions and models across multiple experiments. In complex cases, some intermediate states may be unknown and are often simplified. This approach requires expertise in modeling and data comprehension, as poor decisions at any stage during data analysis can lead to an incorrect kinetic model, resulting in inaccurate results. Here, we introduce DLRN, a new deep learning-based framework, designed to rapidly provide a kinetic reaction network, time constants, and amplitude for the system, with comparable performance and, in part, even better than a classical fitting analysis. We demonstrate DLRN's utility in analyzing multiple timescales datasets with complex kinetics, different 2D systems such as time-resolved spectra and agarose gel electrophoresis data, experimental datasets as nitrogen vacancy and strand displacement circuit (using photoluminescence and transient absorption techniques), even in scenarios where the initial state is a hidden, non-emitting dark state.