A Python tool for parameter estimation of “black box” macro- and micro-kinetic models with Bayesian optimization – petBOA

Abstract

We develop an open-source Python-based Parameter Estimation Tool utilizing Bayesian Optimization (petBOA) with a unique wrapper interface for gradient-free parameter estimation of expensive black-box kinetic models. We provide examples for Python macrokinetic and microkinetic modeling (MKM) tools, such as Cantera and OpenMKM. petBOA leverages surrogate Gaussian processes to approximate and minimize the objective function designed for parameter estimation. Bayesian Optimization (BO) is implemented using the open-source BoTorch toolkit. petBOA employs local and global sensitivity analyses to identify important parameters optimized against experimental data, and leverages pMuTT for consistent kinetic and thermodynamic parameters while perturbing species binding energies within the typical error of conventional DFT exchange-correlation functionals (20-30 kJ/mol). The source code and documentation are hosted on GitHub.

Program summary

Program title: petBOA

Developer's repository link: https://github.com/VlachosGroup/petBOA

Licensing provisions: MIT license

Programming language: Python

External routines: NEXTorch, PyTorch, GPyTorch, BoTorch, Matplotlib, PyDOE2, NumPy, SciPy, pandas, pMuTT, SALib, docker.

Nature of the problem: An open-source, gradient-free parameter estimation of black-box microkinetic modeling tools, such as OpenMKM is lacking.

Solution method: petBOA is a Python-based tool that utilizes Bayesian Optimization and offers a unique wrapper interface for expensive black-box kinetic models. It leverages the pMuTT library for consistent kinetic and thermodynamic parameter estimation and employs both local and global sensitivity analyses to identify crucial parameters.