A third opportunity is to extend the complexity and depth of what is calculated. Typically, many aspects of catalytic materials that are possible to calculate are nevertheless skipped due to computational cost and researcher time. This includes calculating Wulff shapes and dominant surfaces (including the effect of high index surfaces), calculating performance as a function of surface coverage, and calculating side reactions with other species. Machine learning potentials offer the opportunity to calculate such behavior on a more regular basis and for more candidates.