Data-driven discovery of chemical reaction networks

TL;DR

This paper introduces a robust, data-driven framework for reconstructing complete chemical reaction networks from concentration data, enhancing accuracy and noise resilience in mechanistic discovery.

Contribution

It presents a unified integral-based approach for full mechanistic reconstruction of CRNs, with theoretical validation and improved robustness over existing methods.

Findings

Integral formulation improves noise robustness

Enhanced accuracy in rate-law and graph recovery

Theoretical error bounds support method validity

Abstract

We propose a unified framework that allows for the full mechanistic reconstruction of chemical reaction networks (CRNs) from concentration data. The framework utilizes an integral formulation of the differential equations governing the chemical reactions, followed by an automatic procedure to recover admissible mass-action mechanisms from the equations. We provide theoretical justification for the use of integral formulations using analytical and numerical error bounds. The integral formulation is demonstrated to offer superior robustness to noise and improved accuracy in both rate-law and graph recovery when compared to other commonly used formulations. Together, our developments advance the goal of fully automated, data-driven chemical mechanism discovery.