  We present OrbNet Denali, a machine learning model for electronic structure
that is designed as a drop-in replacement for ground-state density functional
theory (DFT) energy calculations. The model is a message-passing neural network
that uses symmetry-adapted atomic orbital features from a low-cost quantum
calculation to predict the energy of a molecule. OrbNet Denali is trained on a
vast dataset of 2.3 million DFT calculations on molecules and geometries. This
dataset covers the most common elements in bio- and organic chemistry (H, Li,
B, C, N, O, F, Na, Mg, Si, P, S, Cl, K, Ca, Br, I) as well as charged
molecules. OrbNet Denali is demonstrated on several well-established benchmark
datasets, and we find that it provides accuracy that is on par with modern DFT
methods while offering a speedup of up to three orders of magnitude. For the
GMTKN55 benchmark set, OrbNet Denali achieves WTMAD-1 and WTMAD-2 scores of
7.19 and 9.84, on par with modern DFT functionals. For several GMTKN55 subsets,
which contain chemical problems that are not present in the training set,
OrbNet Denali produces a mean absolute error comparable to those of DFT
methods. For the Hutchison conformers benchmark set, OrbNet Denali has a median
correlation coefficient of R^2=0.90 compared to the reference DLPNO-CCSD(T)
calculation, and R^2=0.97 compared to the method used to generate the training
data (wB97X-D3/def2-TZVP), exceeding the performance of any other method with a
similar cost. Similarly, the model reaches chemical accuracy for non-covalent
interactions in the S66x10 dataset. For torsional profiles, OrbNet Denali
reproduces the torsion profiles of wB97X-D3/def2-TZVP with an average MAE of
0.12 kcal/mol for the potential energy surfaces of the diverse fragments in the
TorsionNet500 dataset.
