Chemical Kinetics Bayesian Inference Toolbox (CKBIT)

Abstract The robust estimation of chemical kinetic parameters and their associated uncertainty is essential in the field of chemistry and catalysis. The Chemical Kinetics Bayesian Inference Toolbox (CKBIT) is a Python software library introduced to enable users to implement advanced Bayesian inference techniques for kinetic parameter estimation and uncertainty quantification. Leveraging functionalities of other open source Python packages and offering simplified implementation through minimal user-required coding and straightforward Excel input files, CKBIT aspires to make the inference method easily accessible for chemical kinetics. CKBIT provides maximum a posteriori, Markov chain Monte Carlo, and variational inference estimation options. Users may apply these functionalities to estimate activation energies, reaction orders, and pre-exponential terms from chemical reaction data from batch reactors , continuous stirred-tank reactors, and plug flow reactors. The availability of prior distribution specification and the implementation of hierarchical modeling in CKBIT provide a heightened level of accuracy in estimates of kinetic parameters and their uncertainties. Program summary Program title: CKBIT CPC Library link to program files: https://doi.org/10.17632/tnzk2jvffs.1 Developer's repository link: https://github.com/VlachosGroup/ckbit Code Ocean capsule: https://codeocean.com/capsule/8389927 Licensing provisions: MIT license Programming language: Python Nature of problem: Rigorous estimation and uncertainty quantification of kinetic rate parameters are necessary for chemical researchers to create physical models with explicit levels of certainty. Advanced statistical treatments of Bayesian inference meet this need with a high level of rigor. However, current software available for Bayesian inference is complex and nuanced in its implementation, preventing widespread adoption among researchers. Solution method: We present a Python package with a modular approach to applying different Bayesian inference techniques for kinetic rate parameter estimation and uncertainty quantification. Optimal point estimates can be obtained through maximum a posteriori estimation, while full probability distributions of parameters can be generated through Markov chain Monte Carlo estimation or variational inference. The code is straightforward to use in contrast to current Bayesian inference software, and it interfaces with Excel for ease of data entry.