By offering a cost-effective solution, machine-learning approaches could aid such exploration, but they come with their challenges. Machine-learning models are typically successful when trained on large, well-balanced, and diverse datasets, which are difficult to obtain for photocatalysis. Indeed, good MOF photocatalysts are scarce; we carried out some preliminary calculations on structures selected from the QMOF database, and our success rate was very low. Hence, even if we were to compute the photocatalytic properties of many MOFs, we hypothesize that the resulting dataset would likely be imbalanced and lacking diversity due to the current focus of existing MOF databases on gas storage and separation.