Understanding proton transfer and water splitting reactions in nano-porous materials is critical for a wide range of emerging technologies, including hydrogen production through photoelectrochemical water splitting. However, elucidating mechanism and energetics of these processes remains a significant challenge for experimental probes. In this work, we combine large-scale molecular dynamics simulations with machine learning potential derived from first-principles calculations to investigate kinetics of proton transfer in nano-porous TiO2 as a representative photocatalyst material. We developed and applied a deep neural network potential to reconstruct the free energy surface of water dissociation and proton transport for a wide range of pore sizes to elucidate confinement effects. Although proton transfer mechanism is similar to that at a TiO2 interface with bulk water, confinement reduces the activation energy of this process, leading to more frequent proton transfer events. This enhanced proton transfer stems from the contraction of oxygen-oxygen distances dictated by the interplay between confinement and hydrophilic interactions. Our simulations also highlight the importance of the surface topology, where faster proton transport is found in the direction where a unique arrangement of surface oxygens enables the formation of an ordered water chain. Our study highlights the critical competition between kinetic and thermodynamic factors introduced by nano-confinement, suggesting potential strategies for optimization of photocatalytic systems for efficient water splitting reactions.
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