We constructed a machine learning model based on the equivariant e3nn neural network framework that can calculate electron densities for arbitrary DNA structures that well exceed the scope of traditional ab initio quantum calculations. The model is trained on B-DNA base pair steps and shows a remarkably low error (ϵ ρ M L = 0.62% on the test set of 2-mers) comparable to the most accurate machine learning electron density studies. This error does not significantly increase with ). (e) DNA origami triangle , ∼ 340 base pairs, 21658 atoms, 108654 electrons. Illustrations were made using VESTA. base sequence length but tends to flatten out, which suggests the model can predict densities for larger DNA structures with negligible loss of accuracy. The model also extrapolates reasonably well to structural forms beyond B-DNA such as the A-and Z-DNA forms, giving errors of around ϵ ρtrue = 1.75%. The learning curves for this model indicate that the error can be systematically decreased by expanding the training set. To demonstrate the power of the model, we computed electron densities for structures ranging from a DNA duplex to a large DNA origami object containing over 100k electrons on timescales of seconds to minutes.