An alternate way to efficiently leverage data at multiple levels of theory is to construct a multifidelity model, which treats each level of theory as a unique sample. With a substantially expanded dataset size of up to 52,806 samples, we find that the multi-fidelity MEGNet model architecture of Chen et al. achieves significantly lower MAEs than the individual and multi-task models for the "3-fi" (i.e. PBE, HLE17, and HSE06 * ) and "4-fi" (i.e. PBE, HLE17, HSE06 * , and HSE06) models (Table ). These results demonstrate that data at multiple levels of theory can be used to improve the overall model performance, which is especially important for the prediction of band gaps from hybrid functionals that are more computationally demanding to calculate. However, we note that the "2-fi" model (i.e. PBE+HSE06) does not outperform the multi-task model. In future studies, it may be worthwhile to consider additional approaches (e.g. Δ-learning) 108 if only two fidelities are available, especially given the correlation between the PBE and HSE06 functionals (Figure ). The testing set parity plots for each model are presented in Figures , which show that the predictive accuracy generally holds over the range of band gaps, albeit with an increase in scatter towards the low band gap region (e.g. 𝐸 g,DFT < 0.5 eV). The increased error in the low band gap region can likely be traced back to several factors, such as a smaller number of MOFs to train on in this range and a higher fraction of open-shell MOFs whose properties are likely more difficult to predict with ML models. Collectively, we anticipate that the multi-task and multi-fidelity ML models will be a valuable resource for future high-throughput screening studies by minimizing the need to carry out computationally demanding hybrid DFT calculations, particularly if low-fidelity PBE band gap data is readily available (as is the case with the QMOF Database).