Stoichiometrically-informed symbolic regression for extracting chemical reaction mechanisms from data

TL;DR

This paper introduces a stoichiometrically-informed symbolic regression method that accurately uncovers chemical reaction mechanisms and rate constants from time series concentration data, even with noise, advancing data-driven reaction discovery.

Contribution

The paper presents SISR, a novel method combining differential and genetic optimization to identify reaction mechanisms and parameters from data, incorporating stoichiometric constraints for improved accuracy.

Findings

SISR accurately recovers mechanisms in linear and nonlinear reactions.

The method performs well with noisy data.

It outperforms generic data-driven approaches in reaction discovery.

Abstract

A data-driven computational method is introduced to extract chemical reaction mechanisms from time series chemical concentration data. It is realized through the use of dynamic symbolic regression in which a sparse analytical form for a dynamical system is discoverable from the underlying data. We specifically develop the stoichiometrically-informed symbolic regression (SISR) method to address a standing challenge in complex chemical reaction networks: Given a time-series dataset of concentrations of several components, what is the mechanism and the associated rate constants? SISR finds the optimal mechanism, kinetic equations and rate constants by combining differential optimization with a genetic optimization approach that searches a symbolic space of possible reaction mechanisms. Use of SISR in several paradigmatic examples spanning linear and nonlinear reaction schemes results in excellent agreement between true and predicted mechanisms, including when the method is applied to noisy data. The advantages of a stoichiometrically-informed approach such as SISR to address reaction discovery is illustrated through comparison with the use of generic state-of-the-art data-driven approaches.