Oxygen Vacancy Diffusion in Rutile TiO₂: Insight from Deep Neural Network Potential Simulations

Abstract

Defects play a crucial role in the surface reactivity and electronic engineering of titanium dioxide (TiO 2 ). In this work, we have used an active learning method to train deep neural network potentials from the ab initio data of a defective TiO 2 surface. Validations show a good consistency between the deep potentials (DPs) and density functional theory (DFT) results. Therefore, the DPs were further applied on the extended surface and executed for nanoseconds. The results show that the oxygen vacancy at various sites are very stable under 330 K. However, some unstable defect sites will convert to the most favorable ones after tens or hundreds of picoseconds, while the temperature was elevated to 500 K. The DP predicated barriers of oxygen vacancy diffusion were similar to those of DFT. These results show that machine-learning trained DPs could accelerate the molecular dynamics with a DFTlevel accuracy and promote people's understanding of the microscopic mechanism of fundamental reactions.

Site-Sensitivity of CO Oxidation Kinetics Catalyzed by Atomically Dispersed Pt1/TiO2

“…We do not pursue such a reaction mechanism as surface vacancies have been found stable at the rutile TiO 2 surface at 300 K, at which we are calculating TOFs. 68 We note that oxygen diffusion from the bulk to the surface at higher temperatures (∼500 K based on the same study) is plausible and could connect the two catalytic cycles we are currently treating as independent pathways. This interconnection, however, is not examined in the current work.…”

“…The formation of the PtO 4 site from PtO 5 opens up the possibility where the surface oxygen vacancies are filled up with the diffusion of oxygen atoms from the bulk. We do not pursue such a reaction mechanism as surface vacancies have been found stable at the rutile TiO 2 surface at 300 K, at which we are calculating TOFs . We note that oxygen diffusion from the bulk to the surface at higher temperatures (∼500 K based on the same study) is plausible and could connect the two catalytic cycles we are currently treating as independent pathways.…”

Mechanism of photocatalytic CO2 methanation on ultrafine Rh nanoparticles

“…to enable the dissociation of the C-O bond of intermediate species adsorbed on the Rh surface, promoting the production of CH 4 against CO. 4 The high activation energy of oxygen vacancy migration (e.g., 231.6 kJ mol À1 or 2.4 eV in reduced rutile TiO 2 ) 5 usually slows the overall reaction kinetics of CO 2 hydrogenation. 6 The surface Rh atoms interfaced with an oxide support represent only a small fraction of the overall surface Rh atoms, preventing the full capacity of the catalyst Rh NPs from achieving high reaction kinetics. The susceptibility of being reduced by hydrogen at elevated temperatures could sacrifice the stability of oxide supports and the corresponding Rh/oxide composite catalysts.…”