Machine learning full NMR chemical shift tensors of silicon oxides with equivariant graph neural networks

26 October 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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. Re- cently, machine learning has been applied to NMR in the prediction of isotropic chemi- cal 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 ten- sor. Here, we use an equivariant graph neural network (MatTEN) to predict full 29Si chemical shift tensors in silicate materials. The MatTEN 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, MatTEN outperforms the state-of-the-art ma- chine learning models by 47%. MatTEN 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.


Machine Learning

Supplementary weblinks


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.