Insights into computational methods for surface science and catalysis
MetadataShow full item record
The fundamental understanding of both the reactions at catalytic surfaces and the ways in which these surfaces change throughout a catalytic cycle and lifetime are important for both academic and industrial disciplines. To develop these understandings on complex catalytic systems, ultra-high vacuum techniques such as molecular beam studies, temperature programmed desorption, reflection-absorption infrared spectroscopy and Auger electron spectroscopy can be used to study the simplest interactions between gas molecules and surfaces. These interactions can be studied from a bottom-up approach to learn about the system in question, upon which additional complexities can be added. To parallel these experimental techniques, a number of computational methods can be used to support findings and guide new experiments. Ab-initio electronic structure calculations allow for a better understanding of adsorbate-surface interactions, while long timescale dynamic simulations provide insight into the time evolution and kinetics of catalysts and catalytic surfaces. Empirical and machine-learning guided potentials can be developed to lessen computational cost while retaining accuracies comparable to ab-initio calculations. Fitting such potentials ultimately allows for larger calculations to be performed and longer timescales to be simulated. The above methods will be applied to a number of industrially and academically relevant catalytic systems, including studying the interaction of H₂ and CO with Cobalt based Fischer-Tropsch catalysts and the interaction between hydrogen and palladium surfaces. Additionally, the development of a machine learning package to fit and use interatomic potentials will be discussed.