Chemical kinetics consists of the phenomenological framework for the disentanglement of reaction mechanisms, optimization of reaction performance and the rational design of chemical processes. Here, we utilize feed-forward artificial neural networks as basis functions for the construction of surrogate models to solve ordinary differential equations (ODEs) that describe microkinetic models (MKMs). We present an algebraic framework for the mathematical description and classification of reaction networks, types of elementary reaction, and chemical species. Under this framework, we demonstrate that the simultaneous training of neural nets and kinetic model parameters in a regularized multiobjective optimization setting leads to the solution of the inverse problem through the estimation of kinetic parameters from synthetic experimental data. We probe the limits at which kinetic parameters can be retrieved as a function of knowledge about the chemical system states over time, and assess the robustness of the methodology with respect to statistical noise. This surrogate approach to inverse kinetic ODEs can assist in the elucidation of reaction mechanisms based on transient data.

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动力学信息神经网络

化学动力学包括用于解开反应机理，优化反应性能和合理设计化学过程的现象学框架。在这里，我们利用前馈人工神经网络作为替代模型构建的基础函数，以解决描述微动力学模型（MKM）的常微分方程（ODE）。我们为反应网络的数学描述和分类，基本反应的类型以及化学种类提供了一个代数框架。在此框架下，我们证明了在规则化的多目标优化设置中同时训练神经网络和动力学模型参数，可以通过从合成实验数据中估算动力学参数来解决反问题。我们探讨了随着时间的流逝，可以根据有关化学系统状态的知识来获取动力学参数的极限，并评估了该方法相对于统计噪声的稳健性。逆动力学ODE的这种替代方法可以帮助根据瞬态数据阐明反应机理。