Machine-Learning-Driven Exploration of Surface Reconstructions of Reduced Rutile TiO2

Titanium dioxide (TiO2) is widely used as catalyst support due to its stability, tunable electronic properties, and surface oxygen vacancies, which are crucial for catalytic processes such as the reverse water-gas shift (RWGS) reaction. Reduced TiO2 surfaces undergo complex surface reconstructions that endow unique properties, but are computationally challenging to describe. In this study, we utilize machine-learning interatomic potentials (MLIPs) integrated with an active-learning workflow to efficiently explore reduced rutile TiO2 surfaces. This approach enabled the prediction of a phase diagram as a function of oxygen chemical potential, revealing a variety of reconstructed phases, including a previously unreported subsurface shear plane structure. We further investigate the electronic properties of these surfaces and validate our results experimentally. To illustrate their catalytic implications, we examined the behavior of Rh single atoms on the reconstructed surfaces, focusing on CO2 activation, the rate-limiting step in the RWGS reaction. Our findings provide new insights into how extreme surface reductions influence the structural and electronic properties of TiO2, with potential implications for catalyst design.