Automatic data-driven stoichiometry identification and kinetic modeling framework for homogeneous organic reactions

Data-driven and knowledge-driven methods are two approaches used in studying reaction kinetics. This article proposes a hybrid-modeling framework for homogeneous synthesis reactions, which combines the advantages of high level of automation in the data-driven approach and improved accuracy in the knowledge-driven approach. A constrained enumeration method is proposed to generate possible candidate stoichiometries, and dynamic response surface methodology, target factor analysis, and mass balance are used together for identifying stoichiometries one-by-one, without the necessity of an expert-generated candidate list. Then, the previously screened stoichiometries are formed into different groups that represent candidate reaction systems, and the group (or groups) with the greatest likelihood will be identified, based on kinetic fitting and reaction dynamic criteria. This framework has been demonstrated by several examples of different reaction systems. The true reaction stoichiometries are all correctly identified, and the accurate kinetic models are obtained, showing satisfactory performance of the proposed method.

Automated summary

This article proposes a hybrid-modeling framework that combines data-driven and knowledge-driven approaches for studying homogeneous synthesis reactions. By using constrained enumeration, dynamic response surface methodology, target factor analysis, and mass balance, the framework can accurately identify stoichiometries and obtain accurate kinetic models.