Unravelling Complexity in Heterogeneous Catalysis via High Fidelity Kinetic Monte Carlo Simulation

The importance of heterogeneous catalysis in modern applications that enhance the quality of life cannot be overstated: current estimates place the value of the catalyst market at more than $34 billion, and it has been estimated that every $1 spent on a catalyst can generate up to $1000 worth of product. Computational methods are crucial in gaining a fundamental understanding of the physicochemical phenomena resulting in catalytic activity and this understanding can guide the design of improved catalysts. Yet, the complexity encountered in heterogeneous catalysts renders such modelling efforts challenging. Complexity in these systems arises due to the various types of surface sites, which typically have distinct chemical properties; the presence of lateral interactions among reactants and spectators, which affect reaction rates; the multiple possible reaction pathways, which may collectively shape activity and selectivity trends; and finally, emergent phenomena at the meso- and macro-scales, which may lead to highly non-linear behaviours, such as oscillations and pattern formation. In this talk, we will discuss the development of a state-of-the-art modelling framework, which can address these challenges, thereby being able to model complexity in catalytic systems efficiently. The framework is based on the kinetic Monte Carlo (KMC) method and uses concepts from graph theory to represent reaction events, as well as energetic interactions among adsorbed species. Efficient algorithms, employing shared memory as well as distributed memory parallelism, enable the treatment of large domains and complex kinetics. We will further demonstrate the power of this approach on catalytic systems of interest in the environmental and sustainability fields. Our Graph-Theoretical KMC approach will thus be shown able to provide a wealth of information, which can be used to explain experimental observations and obtain insight in the design of materials with desired catalytic properties.