Detailed kinetic models play a crucial role in comprehending and enhancing chemical processes. A cornerstone of these models is accurate thermodynamic and kinetic properties, ensuring fundamental insights into the processes they describe. The prediction of these thermochemical and kinetic properties presents an opportunity for machine learning, given the challenges associated with their experimental or quantum chemical determination. This study reviews recent advancements in predicting thermochemical and kinetic properties for gas-phase, liquid-phase, and catalytic processes within kinetic modeling. We assess the state-of-the-art of machine learning in property prediction, focusing on three core aspects: data, representation, and model. Moreover, emphasis is placed on machine learning techniques to efficiently utilize available data, thereby enhancing model performance. Finally, we pinpoint the lack of high-quality data as a key obstacle in applying machine learning to detailed kinetic models. Accordingly, the generation of large new datasets and further development of data-efficient machine learning techniques are identified as pivotal steps in advancing machine learning’s role in kinetic modeling.

中文翻译：

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用于热化学和动力学特性预测的机器学习应用程序


详细的动力学模型在理解和增强化学过程方面起着至关重要的作用。这些模型的基石是准确的热力学和动力学特性，确保对它们描述的过程有基本的了解。鉴于与实验或量子化学测定相关的挑战，这些热化学和动力学特性的预测为机器学习提供了机会。本研究回顾了在动力学建模中预测气相、液相和催化过程的热化学和动力学特性的最新进展。我们评估了机器学习在财产预测方面的最新技术，重点关注三个核心方面：数据、表示和模型。此外，重点放在机器学习技术上，以有效利用可用数据，从而提高模型性能。最后，我们指出缺乏高质量数据是将机器学习应用于详细动力学模型的主要障碍。因此，大型新数据集的生成和数据高效机器学习技术的进一步发展被确定为推进机器学习在动力学建模中的作用的关键步骤。