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Computing accurate rate constants for catalytic events occurring at thesurface of a given material represents a challenging task with multiplepotential applications in chemistry. To address this question, we propose anapproach based on a combination of the rare event sampling method calledAdaptive Multilevel Splitting (AMS) and ab initio molecular dynamics (AIMD).The AMS method requires a one dimensional reaction coordinate to index theprogress of the transition. Identifying a good reaction coordinate isdifficult, especially for high dimensional problems such a those encountered incatalysis. We probe various approaches to build reaction coordinates such asSupport Vector Machine and path collective variables. The AMS is implemented soas to communicate with a DFT-plane wave code. A relevant case study incatalysis: the change of conformation and the dissociation of a water moleculechemisorbed on the (100) $\gamma$-alumina surface is used to evaluate ourapproach. The calculated rate constants and transition mechanisms are discussedand compared to those obtained by a conventional static approach based on theEyring-Polanyi equation with harmonic approximation. It is revealed that theAMS method may provide rate constants which are smaller than the staticapproach by up to two orders of magnitude due to entropic effects involved inthe chemisorbed water.

Computing Surface Reaction Rates by Adaptive Multilevel Splitting Combined with Machine Learning and Ab Initio Molecular Dynamics