Kinetic models are widely used in simulating the relationship between the input space and the outcome space of a chemical process. Ignoring the computational cost, complete profiling, i.e., performing simulations at all grid points in the input space, would be the best way to understand the model because it provides us with a complete picture of intervariable relationships. Optimization methods that sample favorable input points can only provide narrower views. In this paper, we employ entropic sampling, a statistical physics method, to approximate complete profiling. It is cost-effective and provides a holistic picture of the model, where one can perform post hoc exploratory analyses across any region of the outcome space. Using a kinetic model of the nucleophilic aromatic substitution reaction, we analyze how the failure rate is related to process parameters and elucidate different ways to achieve low failure rates.

中文翻译：

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通过熵采样了解化学过程

动力学模型广泛用于模拟化学过程的输入空间和输出空间之间的关系。忽略计算成本，完整的剖析，即在输入空间中的所有网格点执行模拟，将是理解模型的最佳方式，因为它为我们提供了变量间关系的完整画面。对有利输入点进行采样的优化方法只能提供更窄的视图。在本文中，我们采用熵采样（一种统计物理方法）来近似完整的剖析。它具有成本效益，并提供了模型的整体图景，可以在其中对结果空间的任何区域进行事后探索性分析。使用亲核芳族取代反应的动力学模型，