ArticleThe journal of physical chemistry. A2023
Machine Learning Full NMR Chemical Shift Tensors of Silicon Oxides with Equivariant Graph Neural Networks.
Article in The journal of physical chemistry. A, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Review
- Equivariant Neural Networks Reveal How Host-Guest Interactions ShapeThe journal of physical chemistry letters · 2025Article
- Deep Learning for Hyperpolarized NMR of Intrinsically Disordered Proteins without Resolution Loss: Access to Short-Lived Intermediates.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Article
- Decomposition Analysis of Theoretical Raman Spectra for Efficient Interpretation of Experimental Spectra of Thin-Film Functional Materials.International journal of molecular sciences · 2025Article
- Machine learning ofScientific reports · 2025Article
- Representing Born effective charges with equivariant graph convolutional neural networks.Scientific reports · 2025Article
- Carbohydrate NMR chemical shift prediction by GeqShift employing E(3) equivariant graph neural networks.RSC advances · 2024Article
- Accurate Prediction of NMR Chemical Shifts: Integrating DFT Calculations with Three-Dimensional Graph Neural Networks.Journal of chemical theory and computation · 2024Article
- The need forChemical science · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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
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