ArticleNature communications2018
Chemical shifts in molecular solids by machine learning.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.
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Who cites it
81 citing papers in PubMed.
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- Machine-Learning Ice Spectra: From 1 to 256 Features.Journal of chemical theory and computation · 2026Article
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- Machine learning spectroscopy to advance computation and analysis.Chemical science · 2025Review
- Determination of key functional structures of an amorphous VHL-based SMARCA2 PROTAC.Nature communications · 2025Article
- Exploring the Frontiers of Computational NMR: Methods, Applications, and Challenges.Chemical reviews · 2025Review
- Machine learning ofScientific reports · 2025Article
- The fellowship of the Grignard: 21st century computational tools for hundred-year-old chemistry.Chemical science · 2025Review
- Solid-State Nuclear Magnetic Resonance Spectroscopy for Surface Characterization of Metal Oxide Nanoparticles: State of the Art and Perspectives.Journal of the American Chemical Society · 2025Review
- Predictive crystallography at scale: mapping, validating, and learning from 1000 crystal energy landscapes.Faraday discussions · 2025Article
- Spiers Memorial Lecture: NMR crystallography.Faraday discussions · 2025Article
- The interplay of density functional selection and crystal structure for accurate NMR chemical shift predictions.Faraday discussions · 2025Article
21 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Due to their strong dependence on local atonic environments, NMR chemical shifts are among the most powerful tools for strucutre elucidation of powdered solids or amorphous materials. Unfortunately, using them for structure determination depends on the ability to calculate them, which comes at the cost of high accuracy first-principles calculations. Machine learning has recently emerged as a way to overcome the need for quantum chemical calculations, but for chemical shifts in solids it is hindered by the chemical and combinatorial space spanned by molecular solids, the strong dependency of chemical shifts on their environment, and the lack of an experimental database of shifts. We propose a machine learning method based on local environments to accurately predict chemical shifts of molecular solids and their polymorphs to within DFT accuracy. We also demonstrate that the trained model is able to determine, based on the match between experimentally measured and ML-predicted shifts, the structures of cocaine and the drug 4-[4-(2-adamantylcarbamoyl)-5-tert-butylpyrazol-1-yl]benzoic acid.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.