ConspectusThe transition from fossil fuels to renewable energy requires the development of efficient and cost-effective energy storage technologies. A promising way forward is to harness the energy of intermittent renewable sources, such as solar and wind, to perform (electro)catalytic reactions to generate fuels, thus storing energy in the form of chemical bonds. However, current catalysts rely on the use of expensive, rare, or geographically localized elements, such as platinum. Widespread adoption of new (electro)catalytic technologies hinges on the discovery and development of materials containing earth-abundant elements, which can efficiently catalyze an array of (electro)chemical reactions.In the context of catalysis, descriptors provide correlations between fundamental physical properties, such as the electronic structure, and the resulting catalytic activity. The use of easily accessible descriptors has proven to be a powerful method to advance and accelerate discovery and design of new catalyst materials. The position of the oxygen electronic 2p band center has been proposed to capture the basic physical properties of oxides, including oxygen vacancy formation energy, diffusion barrier of oxygen ions, and work function. Moreover, the adsorption strength of relevant reaction intermediates at the surface of oxides can be strongly correlated with the energy of the oxygen 2p states, which affects the catalytic activity of reactions, such as oxygen electrocatalysis, and oxidative dehydrogenation of organic molecules. Such descriptors for catalytic activity can be used to predict the activity of new catalysts and understand trends and behavior among different catalysts.In this Account, we discuss how the energy of the oxygen 2p states can be used as a descriptor for oxide bulk and surface chemical properties. We show how the oxide redox properties vary linearly with the position of the oxygen 2p band center with respect to the Fermi level, and we discuss how this descriptor can be expanded across different materials and structural families, including possible generalizations to compounds outside oxides. We highlight the power of the oxygen 2p band center to predict the catalytic activity of oxides. We conclude with an outlook examining under which conditions this descriptor can be applied to predict oxide properties and possible opportunities for further refining and accelerating property predictions of oxides by leveraging material databases and machine learning.