Machine Learning Full NMR Chemical Shift Tensors of Silicon Oxides with Equivariant Graph Neural Networks

The nuclear magnetic resonance (NMR) chemical shift tensor
is a
highly sensitive probe of the electronic structure of an atom and
furthermore its local structure. Recently, machine learning has been
applied to NMR in the prediction of isotropic chemical shifts from
a structure. Current machine learning models, however, often ignore
the full chemical shift tensor for the easier-to-predict isotropic
chemical shift, effectively ignoring a multitude of structural information
available in the NMR chemical shift tensor. Here we use an equivariant
graph neural network (GNN) to predict full 29Si chemical
shift tensors in silicate materials. The equivariant GNN model predicts
full tensors to a mean absolute error of 1.05 ppm and is able to accurately
determine the magnitude, anisotropy, and tensor orientation in a diverse
set of silicon oxide local structures. When compared with other models,
the equivariant GNN model outperforms the state-of-the-art machine
learning models by 53%. The equivariant GNN model also outperforms
historic analytical models by 57% for isotropic chemical shift and
91% for anisotropy. The software is available as a simple-to-use open-source
repository, allowing similar models to be created and trained with
ease.