ArticleThe journal of physical chemistry. C, Nanomaterials and interfaces2022
A Machine Learning Model of Chemical Shifts for Chemically and Structurally Diverse Molecular Solids.
Article in The journal of physical chemistry. C, Nanomaterials and interfaces, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Accessible hybrid DFT-quality NMR crystallographyChemical science · 2026Article
- Quantitative Prediction of Exchangeable Proton Chemical Shifts.Nature communications · 2026Article
- Thermodynamics-Informed Machine Learning of Organic Electrode Material Solubility in Nonaqueous Electrolytes.The journal of physical chemistry. B · 2026Article
- A universal machine learning model for the electronic density of states.Digital discovery · 2026Article
- Efficient Machine Learning Prediction of Solvent-DependentJournal of computational chemistry · 2026Article
- Rapid Assignment of Chemical Shifts From Crystal Structures in Solid-State NMR.Angewandte Chemie (International ed. in English) · 2026Article
- Machine-Learning Ice Spectra: From 1 to 256 Features.Journal of chemical theory and computation · 2026Article
- PredictingACS omega · 2026Article
- Review
- Revealing structure and shaping priorities in plant and fungal cell wall architecture via solid-state NMR.Cell surface (Amsterdam, Netherlands) · 2025Review
- PET-MAD as a lightweight universal interatomic potential for advanced materials modeling.Nature communications · 2025Article
- Massive Atomic Diversity: a compact universal dataset for atomistic machine learning.Scientific data · 2025Article
- 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
- 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
- EFG-CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models.Protein science : a publication of the Protein Society · 2024Article
- Polymorph Identification for Flexible Molecules: Linear Regression Analysis of Experimental and Calculated Solution- and Solid-State NMR Data.The journal of physical chemistry. A · 2024Article
- Highly Accurate Prediction of NMR Chemical Shifts from Low-Level Quantum Mechanics Calculations Using Machine Learning.Journal of chemical theory and computation · 2024Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nuclear magnetic resonance (NMR) chemical shifts are a direct probe of local atomic environments and can be used to determine the structure of solid materials. However, the substantial computational cost required to predict accurate chemical shifts is a key bottleneck for NMR crystallography. We recently introduced ShiftML, a machine-learning model of chemical shifts in molecular solids, trained on minimum-energy geometries of materials composed of C, H, N, O, and S that provides rapid chemical shift predictions with density functional theory (DFT) accuracy. Here, we extend the capabilities of ShiftML to predict chemical shifts for both finite temperature structures and more chemically diverse compounds, while retaining the same speed and accuracy. For a benchmark set of 13 molecular solids, we find a root-mean-squared error of 0.47 ppm with respect to experiment for
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