Automatic intrinsic reaction mechanism discovery through hybrid modelling

高效催化剂的开发是过程工程与反应工程相结合的多学科研究。具体来说，速率表达式一直是表征催化剂性能的动力学建模的中心。虽然表观催化动力学可以用经验表达式估计，但其内在的反应机制仍然未知。这阻碍了对反应现象的基本理解和新型催化剂的设计。通过融合数据智能和动力学建模，该项目将构建一个数字框架，通过“破解”经验速率表达式的参数和结构来发现内在的反应机制。这将使开发具有特定应用所需性能的定制催化剂成为可能。

Developing high-efficiency catalysts is a multidisciplinary research between process engineering and reaction engineering. Specifically, rate expressions have been the centre of kinetic modelling to characterise catalyst performance. Although the apparent kinetics of catalysis can be estimated using empirical expressions, their intrinsic reaction mechanisms still remain unknown. This prevents fundamental understandings of reaction phenomena and design of novel catalysts. By merging data intelligence and kinetic modelling, this project will construct a digital framework to discover intrinsic reaction mechanisms by 'cracking' parameters and structures of empirical rate expressions. This will enable the development of tailored-made catalysts with desired properties for specific applications.