  We have developed a deep learning algorithm for chemical shift prediction for
atoms in molecular crystals that utilizes an atom-centered Gaussian density
model for the 3D data representation of a molecule. We define multiple channels
that describe different spatial resolutions for each atom type that utilizes
cropping, pooling, and concatenation to create a multi-resolution 3D-DenseNet
architecture (MR-3D-DenseNet). Because the training and testing time scale
linearly with the number of samples, the MR-3D-DenseNet can exploit data
augmentation that takes into account the property of rotational invariance of
the chemical shifts, thereby also increasing the size of the training dataset
by an order of magnitude without additional cost. We obtain very good agreement
for 13C, 15N, and 17O chemical shifts, with the highest accuracy found for 1H
chemical shifts that is equivalent to the best predictions using ab initio
quantum chemistry methods.
