With the goal of reducing the number of DFT calculations needed in future high-throughput computational screening studies, we have evaluated the performance of several ML models that can predict MOF band gaps from graph representations of their three-dimensional structures (for the prediction of partial atomic charges, we refer the reader to several ML models 103-105 that have been shown to accurately predict PBEquality DDEC6 and CM5 charges for MOFs). Using MatDeepLearn, we first trained individual graph neural networks for each DFA and found that they performed well at predicting DFT-computed band gaps compared to a baseline model that simply predicts the mean of the dataset for each entry (Table ). Prior work on the QMOF Database showed that a crystal graph convolutional neural network (CGCNN) model 106 could predict PBE band gaps with a comparable accuracy, and it is reassuring that relatively low testing-set MAEs on the order of 0.24 -0.29 eV can be obtained for the more accurate DFAs (i.e., HLE17, HSE06 * , HSE06). Overall, the graph neural network trained on PBE band gap data performs better than the graph neural networks trained on the HLE17, HSE06 * , or HSE06 datasets, which can likely be attributed to the greater number of data points available for training with PBE. Despite similar training set sizes for the HLE17, HSE06 * , and HSE06 levels of theory, the model based on HSE06 data has the largest testing set MAE of 0.29 eV, which may be attributed in part to a wider range of possible band gap values and a greater overlap in the band gap distributions for the closed-and open-shell frameworks. Table . Individual, multi-task, and multi-fidelity model performance. The individual models represent four separate models that are each trained on band gaps at a single level of theory. The multi-task model is a single model that is trained on and predicts band gaps at all four levels of theory simultaneously. The multi-fidelity models combine data from different levels of theory without all samples needing to have band gaps at each level of theory. The 2-fi, 3-fi, and 4-fi models are trained/tested on PBE+HSE06, PBE+HLE17+HSE06, and PBE+HLE17+HSE06 * +HSE06 data, respectively. A baseline model that simply predicts the mean value of the dataset is shown for reference. The dataset sizes refer to the entire available dataset, which is split 80:5:15 train:validation:test. The mean absolute errors (MAEs) are shown for the testing set. Next, we considered various approaches that could make more efficient use of the available band gap data obtained with different functionals. Starting with a multi-task learning approach that predicts band gaps for all four DFAs simultaneously using a single model architecture, perceptible but minor improvements to the model performance are obtained (Table ). While more convenient to use than multiple individual models if multiple band gap estimates are desired, an inherent drawback of the multi-task learning method is that the training process requires structures that have band gaps computed for all DFAs of interest, which limits the amount of data that can be used.