Nevertheless, quantum density functional theory (DFT) calculations have been used in numerous DNA studies. In particular, they have been useful for understanding the energetics of various DNA binding complexes with proteins and metals. Benefits from studying these complexes range from a deeper understanding of fundamental biological processes to the development of technologically advanced biomedical applications. However, due to the high computational cost of DFT, complexes tend to be modeled in a limited capacity, restricted to modeling a few DNA base pairs or including only small portions of the complex. A DFT benchmarking study was able to accurately investigate DNA stacking interactions at the base pair step level. In addition, a recent DFT study was able to model a full turn of A-DNA in a vacuum environment at the hybrid functional level. But given the steep scaling of DFT, calculations on larger DNA structures would be extremely costly if not unfeasible. As such, molecular simulations of full DNA structures typically rely on classical force fields or coarse-grained methods. Recently, machine learning approaches have been developed to sidestep the prohibitive scaling of quantum chemistry methods. These approaches aim to construct an alternative model to solving Schrödinger's equation, making them a promising avenue for studying large biological systems that would typically fall outside the range of quantum simulations. To date, machine learning methods have been applied to predict accurate energies , forces , and electron densities on mostly small molecules and crystals. For DNA in particular, machine learning has been used to develop a DFT functional that correctly describes charge delocalization in base pairing. But a machine learning model has not yet been developed to predict properties for arbitrary, large-scale DNA structures.