Predicting Density Functional Theory-Quality Nuclear Magnetic Resonance Chemical Shifts via Δ-Machine Learning
Author:
Affiliation:
1. Department of Chemistry, University of California, Riverside, Riverside, California 92521, United States
Funder
Division of Chemistry
Publisher
American Chemical Society (ACS)
Subject
Physical and Theoretical Chemistry,Computer Science Applications
Link
https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.0c00979
Reference84 articles.
1. Combining solid-state NMR spectroscopy with first-principles calculations – a guide to NMR crystallography
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4. A guide to small-molecule structure assignment through computation of (1H and 13C) NMR chemical shifts
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