In order to extend our previous gas-phase DNA machine learning model to account for explicit solvent interactions, the training procedure had to be modified. To accommodate the solvent molecules into the training set, the fundamental base-pair step training structures were split into smaller overlapping fragments that sample the key interactions for a solvated DNA system. By comparing the predictions of machine learning models trained on different training sets, we gain insight into how machine learning models learn and make predictions from their training data. Another benefit from using smaller training fragments is that training on larger basis sets and higher levels of theory becomes more computationally tractable. It has been shown that larger basis sets are needed to calculate accurate forces from electron densities . The efficient calculation of forces can be useful for structural optimization and ab initio molecular dynamics simulations, leading to potential applications in structural prediction and biomolecular binding studies.