Water Dissociation at the Water-rutile TiO(110) Interface from Ab initio-based Deep Neural Network Simulations

The interaction of water with TiO surfaces is of crucial importance in various scientific fields and applications, from photocatalysis for hydrogen production and the photooxidation of organic pollutants to self-cleaning surfaces and bio-medical devices. In particular, the equilibrium fraction of water dissociation at the TiO-water interface has a critical role in the surface chemistry of TiO, but is difficult to determine both experimentally and computationally. Among TiO surfaces, rutile TiO(110) is of special interest as the most abundant surface of TiO's stable rutile phase. While surface-science studies have provided detailed information on the interaction of rutile TiO(110) with gas-phase water, much less is known about the TiO(110)-water interface, which is more relevant to many applications. In this work, we characterize the structure of the aqueous TiO(110) interface using nanosecond timescale molecular dynamics simulations with ab initio-based deep neural network potentials that accurately describe water/TiO(110) interactions over a wide range of water coverages. Simulations on TiO(110) slab models of increasing thickness provide insight into the dynamic equilibrium between molecular and dissociated adsorbed water at the interface and allow us to obtain a reliable estimate of the equilibrium fraction of water dissociation. We find a dissociation fraction of 22 ± 6% with an associated average hydroxyl lifetime of 7.6 ± 1.8 ns These quantities are both much larger than corresponding estimates for the aqueous anatase TiO(101) interface, consistent with the higher water photooxidation activity that is observed for rutile relative to anatase.