The temporal analysis of products (TAP) technique produces extensive transient kinetic data sets, but it is challenging to translate the large quantity of raw data into physically interpretable kinetic models, largely due to the computational scaling of existing numerical methods for fitting TAP data. In this work, we utilize kinetics-informed neural networks (KINNs), which are artificial feedforward neural networks designed to solve ordinary differential equations constrained by micro-kinetic models, to model the TAP data. We demonstrate that, under the assumption that all concentrations are known in the thin catalyst zone, KINNs can simultaneously fit the transient data, retrieve the kinetic model parameters, and interpolate unseen pulse behavior for multi-pulse experiments. We further demonstrate that, by modifying the loss function, KINNs maintain these capabilities even when precise thin-zone information is unavailable, as would be the case with real experimental TAP data. We also compare the approach to existing optimization techniques, which reveals improved noise tolerance and performance in extracting kinetic parameters. The KINNs approach offers an efficient alternative for TAP analysis and can assist in interpreting transient kinetics in complex systems over long timescales.

翻译：产物时间分析（TAP）技术可产生大量的瞬态动力学数据集，但将大量原始数据转化为具有物理可解释性的动力学模型极具挑战性，这主要源于现有用于拟合TAP数据的数值方法在计算规模上的限制。在本工作中，我们采用动力学信息神经网络（KINNs）——一种设计用于求解受微动力学模型约束的常微分方程的人工前馈神经网络——来对TAP数据进行建模。我们证明，在假设催化剂薄层区域内所有浓度均已知的条件下，KINNs能够同时拟合瞬态数据、反演动力学模型参数，并对多脉冲实验中未观测到的脉冲行为进行插值。我们进一步证明，通过修改损失函数，即使在无法获得精确薄层区域信息的情况下（如真实实验TAP数据中常见的情形），KINNs仍能保持这些能力。我们还比较了该方法与现有优化技术，结果表明其在提取动力学参数方面具有更好的噪声容忍度和性能。KINNs方法为TAP分析提供了一种高效的替代方案，并有助于在长时间尺度上解释复杂系统中的瞬态动力学行为。