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Mostly covering 2018 to 2022 This article describes a personal selection of recent misassigned structures of natural products and their revision with the aid of DU8ML, a machine learning-augmented DFT computational method for fast and accurate calculations of solution NMR chemical shifts and spin–spin coupling constants.
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This content will become publicly available on March 31, 2026
Predicting Solid-state NMR Observables via Machine Learning
Machine learning is becoming increasingly important in the prediction of nuclear magnetic resonance (NMR) chemical shifts and other observable properties. This chapter provides an introduction to the construction of machine learning (ML) models for predicting NMR properties, including the discussion of feature engineering, common ML model types, Δ-ML and transfer learning, and the curation of training and testing data. Then it discusses a number of recent examples of ML models for predicting chemical shifts and spin–spin coupling constants in organic and inorganic species. These examples highlight how the decisions made in constructing the ML model impact its performance, discuss strategies for achieving more accurate ML models, and present some representative case studies showing how ML is transforming the way NMR crystallography is performed.
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- Award ID(s):
- 1955554
- PAR ID:
- 10629166
- Publisher / Repository:
- Royal Society of Chemistry
- Date Published:
- ISBN:
- 978-1-83767-066-6
- Page Range / eLocation ID:
- 224 to 255
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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