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Automatic kinetic model generation and selection based on concentration versus time curves
Author(s) -
Nagy Tibor,
Tóth János,
Ladics Tamás
Publication year - 2020
Publication title -
international journal of chemical kinetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.341
H-Index - 68
eISSN - 1097-4601
pISSN - 0538-8066
DOI - 10.1002/kin.21335
Subject(s) - kinetic energy , set (abstract data type) , simple (philosophy) , chemistry , action (physics) , selection (genetic algorithm) , biological system , model selection , statistical physics , algorithm , computer science , artificial intelligence , physics , classical mechanics , philosophy , epistemology , quantum mechanics , biology , programming language
The goal of the paper is to automatize the construction and parameterization of kinetic reaction mechanisms that can describe a set of experimentally measured concentration versus time curves. Using the framework and theorems of formal reaction kinetics, first, we build a set of possible mechanisms with a given number of measured and unmeasured (real or fictitious) species and reaction steps that fulfill some chemically reasonable requirements. Then we fit all the corresponding mass‐action kinetic models and offer the best one to the chemist to help explain the underlying chemical phenomenon or to use it for predictions. We demonstrate the use of the method via two simple examples: on an artificial, simulated set of data and on a small real‐life data set. The method can also be used to do a kind of lumping to generate a model that can reproduce the simulation results of a detailed mechanism with less species and thereby can largely accelerate spatially inhomogeneous simulations.