Fig. 1: Oxygen vacancy distribution on rutile TiO2(110) obtained by various methods.
From: Machine learning-based prediction of polaron-vacancy patterns on the TiO2(110) surface
a Schematic representation of the most favorable VO distribution in non-polaronic DFT calculations as obtained from a 6 × 4 (~1.8 × 2.6 nm2) supercell. The schematic depiction is generated by showing the Obr bridging atoms as black regions and Ti5c rows and VO as white. The inset displays the structural model of rutile TiO2(110). The distance maximizing VO distribution (six sites in row, three sites in adjacent row) and the 6 × 4 supercell are indicated. b Unoccupied-states, constant-current STM image of a clean, reduced rutile TiO2(110) surface (imaging parameters in the Figure) depicting Ti5c rows and VOs as bright, while Obr rows are depicted as dark. More details on the contrast formation are given in the Methods. Locally low and high VO concentrations (c\({}_{{{{\rm{{V}}}_{\mathrm{O}}}}}\)) areas are marked with solid and dashed red boxes, respectively. The crystalographic directions are consistent in all panels. c ML-predicted schematic representation of surface oxygen vacancy distribution, where the interaction of surface and subsurface polarons (PolS0 and PolS1, respectively) and VOs are modeled in a 54 × 24 (~16 × 16 nm2) supercell. Orange and yellow markers show the position of surface and subsurface polarons in the ML prediction.
