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.

中文翻译：

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化学动力学贝叶斯推断工具箱（CKBIT）

化学动力学参数及其相关不确定性的可靠估算对于化学和催化领域至关重要。化学动力学贝叶斯推断工具箱（CKBIT）是一个Python软件库，旨在使用户能够实施先进的贝叶斯推断技术来进行动力学参数估计和不确定性量化。利用其他开源Python软件包的功能，并通过最少的用户要求的编码和简单的Excel输入文件提供简化的实现，CKBIT希望使推理方法易于用于化学动力学。CKBIT提供了最大的后验，马尔可夫链蒙特卡洛和变分推断估计选项。用户可以应用这些功能来估算活化能，反应顺序，以及来自间歇反应器，连续搅拌釜反应器和活塞流反应器的化学反应数据的指数前项。先前分配规范的可用性以及CKBIT中分层建模的实现为动力学参数及其不确定性的估计提供了更高的准确性。

计划摘要

节目名称： CKBIT

CPC库链接到程序文件： https : //doi.org/10.17632/tnzk2jvffs.1

开发人员的资料库链接： https : //github.com/VlachosGroup/ckbit

Code Ocean太空舱： https : //codeocean.com/capsule/8389927

许可条款： MIT许可

编程语言： Python

问题的性质：动力学速率参数的严格估计和不确定性量化对于化学研究人员创建具有明确确定性水平的物理模型是必要的。贝叶斯推理的高级统计处理可以高度严格地满足这一需求。但是，当前可用于贝叶斯推理的软件非常复杂且在实现方面存在细微差别，从而阻止了研究人员的广泛采用。

解决方法：我们提供了一种Python软件包，该软件包具有模块化方法，可将不同的贝叶斯推断技术应用于动力学速率参数估计和不确定性量化。最佳点估计可以通过最大后验估计来获得，而参数的全部概率分布可以通过马尔可夫链蒙特卡罗估计或变分推断来生成。与当前的贝叶斯推理软件相比，该代码易于使用，并且可与Excel交互以简化数据输入。