# Automatic model generation

## TL;DR

This paper presents an automated approach for selecting and fitting chemical mechanisms to measurement data, enabling efficient model simplification and better understanding of chemical processes.

## Contribution

It introduces a method to generate and fit mechanisms automatically, facilitating model selection and lumping in chemical kinetics.

## Key findings

- Successfully applied to artificial data
- Demonstrated on real-life small dataset
- Effective in mechanism simplification

## Abstract

The goal of the paper is to automatize the selection of mechanisms which are able to describe a set of measurements. In order to do so first we construct a set of possible mechanism fulfilling chemically reasonable requirements with a given number of species and reaction steps. Then we try to fit all the mechanisms, and offer the best fitting one to the chemist for further analysis. The method can also be used to a kind of lumping: to reproduce the results of a big mechanism using a smaller one, with less number of species. We show two applications: one on an artificial example and another one on a small real life data.