The explosion in the use and development of machine learning (ML) methods to solve scientific problems has spread to and become firmly entrenched in the field of quantum molecular modeling. One of the key benefits of machine learning approaches in this realm is that restrictive computational scaling limitations stemming from the cost of solving Schrödinger's equation can be bypassed. This opens up the possibility of studying large biological macromolecules such as DNA, for which the broad application of quantum modeling has been limited by prohibitively large system sizes . To date, machine learning methods have been applied to predict accurate potential energy surfaces and forces and electron densities of mostly small molecules and crystals. For DNA specifically, a machine learning method has been used to develop a density functional theory (DFT) functional that correctly describes charge delocalization in base pairing .