Although the use of neural networks is now widespread in many practical applications, their use as predictive models in scientific work is often challenging due to the high amounts of data required to train the models and the unreliable predictive performance when extrapolating outside of the training dataset. In this work, we demonstrate a method by which our knowledge of polymerization processes in the form of kinetic models can be incorporated into the training process in order to overcome both of these problems in the modelling of polymerization reactions. This allows for the generation of accurate, data-driven predictive models of polymerization processes using datasets as small as a single sample. This approach is demonstrated for an example solution polymerization process where it is shown to significantly outperform purely inductive learning systems, such as conventional neural networks, but can also improve predictions of existing first principles kinetic models.

中文翻译：

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聚合物化学信息神经网络 （PCINN），用于聚合过程的数据驱动建模


尽管神经网络现在在许多实际应用中被广泛使用，但由于训练模型需要大量数据，并且在训练数据集之外进行推断时预测性能不可靠，因此在科学工作中将其用作预测模型通常具有挑战性。在这项工作中，我们展示了一种方法，通过这种方法，我们可以将动力学模型形式的聚合过程知识纳入训练过程，以克服聚合反应建模中的这两个问题。这允许使用小至单个样品的数据集生成准确的、数据驱动的聚合过程预测模型。这种方法通过一个示例解聚合过程进行了演示，其中显示它明显优于纯归纳学习系统，例如传统神经网络，但也可以改进对现有第一性原理动力学模型的预测。