Chemical processes at metal oxide - water interfaces are of central importance in geochemistry, biology, and energy technologies. A better understanding of these processes is essential for progress in these fields. Computational modeling is indispensable to accomplishing this task because complexity and disorder often makes it difficult to extract atomistic information from experiments. Balancing computational cost and accuracy, simulation schemes based on efficient machine learning representations of the potential energy surface predicted by ab initio calculations have become increasingly popular over the last decade. In this talk, I will discuss recent applications of deep neural network based molecular dynamics simulations to understand the structure and chemistry of aqueous oxide interfaces. Specific topics will include proton transfer processes on surfaces of relevance in photo-electrochemistry, such as TiO
2 and IrO
2, and the influence of the adsorption of organic species on the water structure and wettability of the interface.
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