Kinetic process models are widely applied in science and engineering, including atmospheric, physiological and technical chemistry, reactor design, or process optimization. These models rely on numerous kinetic parameters such as reaction rate, diffusion or partitioning coefficients. Determining these properties by experiments can be challenging, especially for multiphase systems, and researchers often face the task of intuitively selecting experimental conditions to obtain insightful results. We developed a numerical compass (NC) method that integrates computational models, global optimization, ensemble methods, and machine learning to identify experimental conditions with the greatest potential to constrain model parameters. The approach is based on the quantification of model output variance in an ensemble of solutions that agree with experimental data. The utility of the NC method is demonstrated for the parameters of a multi-layer model describing the heterogeneous ozonolysis of oleic acid aerosols. We show how neural network surrogate models of the multiphase chemical reaction system can be used to accelerate the application of the NC for a comprehensive mapping and analysis of experimental conditions. The NC can also be applied for uncertainty quantification of quantitative structure–activity relationship (QSAR) models. We show that the uncertainty calculated for molecules that are used to extend training data correlates with the reduction of QSAR model error. The code is openly available as the Julia package KineticCompass.

中文翻译：

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用于化学动力学和分子性质估计实验设计的数值指南针

动力学过程模型广泛应用于科学和工程领域，包括大气、生理和技术化学、反应器设计或过程优化。这些模型依赖于许多动力学参数，例如反应速率、扩散或分配系数。通过实验确定这些特性可能具有挑战性，特别是对于多相系统，研究人员经常面临直观选择实验条件以获得富有洞察力的结果的任务。我们开发了一种数值罗盘（NC）方法，该方法集成了计算模型、全局优化、集成方法和机器学习，以识别最有可能约束模型参数的实验条件。该方法基于与实验数据一致的解决方案集合中模型输出方差的量化。 NC 方法的实用性通过描述油酸气溶胶非均相臭氧分解的多层模型的参数得到了证明。我们展示了如何使用多相化学反应系统的神经网络替代模型来加速 NC 的应用，以对实验条件进行全面的映射和分析。 NC 还可用于定量构效关系 (QSAR) 模型的不确定性量化。我们表明，为用于扩展训练数据的分子计算的不确定性与 QSAR 模型误差的减少相关。该代码作为 Julia 包 KineticCompass 公开提供。