# Simplest mechanism builder algorithm (simba): an automated microkinetic model discovery tool

## TL;DR

SiMBA is an automated tool that builds microkinetic models from kinetic data, simplifying the process of discovering reaction mechanisms.

## Contribution

SiMBA introduces a data-first, automated approach for generating and selecting microkinetic models using mechanism enumeration and information criteria.

## Key findings

- SiMBA successfully predicts intermediates in case studies like aldol condensation and fructose dehydration.
- The tool balances model accuracy and complexity using information criteria for model selection.
- SiMBA accelerates chemical process modeling but requires expert input for complex systems.

## Abstract

Microkinetic models are key for evaluating industrial processes' efficiency and chemicals' environmental impact. Manual construction of these models is difficult and time-consuming, prompting a shift to automated methods. This study introduces SiMBA (Simplest Mechanism Builder Algorithm), a novel approach for generating microkinetic models from kinetic data. SiMBA operates through four phases: mechanism generation, mechanism translation, parameter estimation, and model comparison. Our approach systematically proposes reaction mechanisms, using matrix representations and a parallelized backtracking algorithm to manage complexity. These mechanisms are then translated into microkinetic models represented by ordinary differential equations, and optimi/zed to fit available data. Models are compared using information criteria to balance accuracy and complexity, iterating until convergence to an optimal model is reached. Case studies on an aldol condensation reaction, and the dehydration of fructose demonstrate SiMBA's effectiveness in distilling complex kinetic behaviors into simple yet accurate models. While SiMBA predicts intermediates correctly for all case studies, it does not chemically identify intermediates, requiring expert input for complex systems. Despite this, SiMBA significantly enhances mechanistic exploration, offering a robust initial mechanism that accelerates the development and modeling of chemical processes. By automating microkinetic model generation from a data-first approach, SiMBA opens new avenues for future research in automated mechanism discovery.

SiMBA automates microkinetic model discovery: it enumerates candidate mechanisms, fits ODE rate laws to data, and selects the simplest accurate pathway via information criteria.