Data-driven discovery of chemical reaction mechanisms from limited concentration profiles

Automating the discovery of chemical reaction mechanisms can increase the efficiency of the use of experimental data to obtain chemical knowledge. In this study, a sparse identification approach was employed to determine reaction mechanisms, providing accurate kinetic models while preventing overfitting. The main advantage of the proposed approach over conventional sparse identification algorithms is that it can be applied to cases with limited concentration profiles, which often occur for chemical reactions involving untraceable intermediates. This strategy enables the automated discovery of reaction mechanisms without relying on heuristic kinetic models, as the only assumption required is the composition of the intermediates. The application to the autocatalytic reduction of manganese oxide ions revealed that the experimental data can be sufficiently represented by 11 elementary steps involving 8 chemical species. The strategy to extract physical models from limited temporal profiles has potential for automated scientific discovery in multiple disciplines, where comprehensive measurements are impractical.