Proton hopping is a key reactive process within zeolite catalysis, however the accurate determination of its kinetics poses major challenges both for theoreticians and experimentalists. Nuclear quantum effects (NQEs) are known to influence the structure and dynamics of protons, but their inclusion in a rigorous way through the path integral molecular dynamics (PIMD) formalism was so far beyond reach for zeolite catalyzed processes due to the excessive computational cost of evaluating all forces

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... and energies at the Density Functional Theory (DFT) level. Herein, we overcome this limitation, by training first a reactive machine learning potential (MLP) that can reproduce with high fidelity the DFT potential energy surface of proton hopping around the first Al coordination sphere in the H-CHA zeolite. The MLP offers an immense computational speedup, which enabled us to derive accurate reaction kinetics for the proton hopping reaction. NQEs are explicitly accounted for through PIMD simulations, and the kinetics is derived using the reactive flux formalism allowing also barrier recrossings, thus going beyond standard transition state theory. More than 0.6 μs of simulation time was necessary, which is far beyond reach of any standard DFT approach. NQEs are found to significantly impact the proton hopping kinetics up to ~473 K. These proof-of-concept results open the window to include in a more systematic way NQEs for heterogeneous catalyzed processes.