Kinetic Consequences of Quasi-Harmonic Entropies Calculated with Machine Learning Interatomic Potentials for Microkinetic Modeling

Microporous catalysts are ubiquitous in chemical processes including sustainable transformations of biobased feedstocks into fuels and fine chemicals. The mechanistic insights needed to design next-generation microporous catalysts can be obtained with ab initio simulations coupled with microkinetic modeling, yet active site confinement complicates an accurate determination of adsorbate entropies, which, in turn, affect predictions of rate and equilibrium constants. In this study, we developed a machine learning force field (MLFF) strategy to rapidly predict temperature-dependent quasi-harmonic adsorbate entropies in zeolite Beta, reducing the number of compute-intensive ab initio molecular dynamics calculations needed to construct a microkinetic model. These entropies directly impacted the kinetics of a model parallel reaction mechanism. We chose lactic acid dehydration to acrylic acid on aluminosilicate zeolite Beta to explore the pathway dependence of unselective product formation and initial deactivation mechanisms using microkinetic modeling with our MLFF entropy strategy. The resulting quasi-harmonic entropy approximations led to shifts in steady-state coverages that impacted reaction orders and product selectivity. At low lactic acid partial pressures, concerted monomolecular decarbonylation is favored over Brønsted acid sites, which then shifts at high lactic acid partial pressures to concerted bimolecular condensations into lactic acid oligomers. Sequential pathways mediated by adsorbed alkoxide or carbonyl intermediates have no kinetic relevance at these conditions. These findings provide a strategy to integrate quasi-harmonic entropies into microkinetic modeling that is scalable with reaction temperature and applicable to a wide range of catalysts and catalytic cycles.