The results from these improvements are shown in Figure . As seen, there are only small differences in performance between the default settings and any potential improvements to the ML settings. For T 1 , hyperparameter optimization provides minimal improvement, while including additional features or adding multitasking reduces accuracy. For S 1 , both hyperparameter optimization and multitasking marginally improve performance. There is thus a tradeoff in using multitasking as it could reduce accuracy for T 1 predictions but improve accuracy for S 1 , while also reducing overall computation time. Because of the time savings of the multi-task model and previous works showcasing the benefits of multi-property prediction, this was used for ML for the following sections. Hyperparameter optimization was not performed for the following models, due to the only marginal improvement seen. Based on this analysis, a larger-scale calibration model can now be developed using CP MPNN.