In recent years ML has been applied widely, with a goal of accelerating quantum chemical calculations that would otherwise have large computational costs. Calculating electronic properties with traditional methods can be computationally expensive and take between hours to weeks to finish, depending on the size of the system and the type of calculation. ML has shown a great potential in calculating electronic structure properties, drug discovery, materials research, and more . Training a ML model is also time consuming as well, since it requires a large data set for training and finding an accurate ML method and representation for that specific application can be exhaustive, but once a model has been properly trained, evaluation for new calculations can be performed in seconds or less.