Discovering intrinsic catalytic reaction mechanisms: using machine learning to 'crack' rate expressions

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Developing high-efficiency and sustainable catalysts is one of the research priorities that lies at the interface between chemistry and chemical engineering. For most industrial reactions involving multiple phases, rate expressions such as the Langmuir-Hinshelwood equation have been the centre of long-standing kinetic models to quantitatively characterise heterogeneous catalysis. Although the apparent kinetics of heterogeneous catalysis can be estimated using these expressions for industrial applications, the intrinsic reaction kinetics are still not well understood. This not only limits the fundamental understanding of the catalytic reaction phenomena, but also mitigates the possibility of a tailored-made design of new catalysts. By merging data analytics and kinetic understanding, this project will discover the intrinsic catalytic reaction mechanism by exploiting recent advances from the machine learning community to analyse and decompose the empirical parameters and structures of rate expressions. Furthermore, aided by these new understandings, this project will design a methodology to develop tailored-made catalysts with desired properties for specific applications. It will be important to focus on well-established simple reactions, where many data already exist in the literature to give a good head start. Simple reactions will allow more emphasis on intrinsic catalyst properties, particularly when exploring multifunctional catalysts. Based on a recent Perspectives article in Green Chemistry [1], amide reduction appears to be a suitable candidate to benefit from the above approach. To design a highly active heterogeneous catalyst for the selective hydrogenation of multifunctional amides is a considerable challenge. Successful PhD candidates will have the opportunity to learn both machine learning and mathematical programming techniques as well as a range of highly sophisticated experimental skills. [1] Green Chem, 2018, 20, 5082