Towards real dynamics in heterogeneous catalysis using machine learning interatomic potential simulations

This thesis focuses on MLIP-based simulations towards realistic modelling of heterogeneous catalysis systems, which are performed either with a long time-scale or using a large-scale structure model. Chapter 1 discusses why realistic modelling is necessary for heterogeneous catalysis. Chapter 2 introduces the theoretical background and the computational approach, including some basic concepts of state-of-the-art ML techniques, which are frequently encountered in computational studies.

Chapter 3 demonstrates an efficient framework I have been actively developing to automate the structure sampling and the MLIP training. Chapter 4 presents a study on CO oxidation on Pt(111) surface using Ab Initio Molecular Dynamics (AIMD) to reveal its dynamical behaviour. Chapter 5 proposes an algorithm that I use MLIPs to accelerate enhanced sampling methods for efficient Molecular Dynamics (MD)-based Free Energy Calculation (FEC).

Chapter 6 studies the Pt surface oxidation with large-scale Grand Canonical Monte Carlo (GCMC) simulations using MLIP. Chapter 7 concludes this thesis and envisages how MLIP could further propel computational catalysis research in the near future.