  The multifaceted physics of oxides is shaped by their composition and the
presence of defects, which are often accompanied by the formation of polarons.
The simultaneous presence of polarons and defects, and their complex
interactions, pose challenges for first-principles simulations and experimental
techniques. In this study, we leverage machine learning and a first-principles
database to analyze the distribution of surface oxygen vacancies (V$_{\rm O}$)
and induced small polarons on rutile TiO$_2$(110), effectively disentangling
the interactions between polarons and defects. By combining neural-network
supervised learning and simulated annealing, we elucidate the inhomogeneous
V$_{\rm O}$ distribution observed in scanning probe microscopy (SPM). Our
innovative approach allows us to understand and predict defective surface
patterns at previously inaccessible length scales, identifying the specific
role of individual types of defects. Specifically, surface-polaron-stabilizing
V$_{\rm O}$-configurations are identified, which could have consequences for
surface reactivity.
