NMRexp: A database of 3.3 million experimental NMR spectra

Although NMR spectroscopy is one of the most fundamental methods for structural characterization in organic chemistry, high-quality and large-scale experimental NMR datasets remain a critical bottleneck for training robust, generalizable spectral-prediction and structure-elucidation models. Here, we introduce NMRexp, an open, curated collection of 3.3 million experimental NMR records for six nuclei (<sup>1</sup>H, <sup>13</sup>C, <sup>19</sup>F, <sup>31</sup>P, <sup>29</sup>Si, <sup>11</sup>B), extracted from nearly 200 thousand Supporting Information documents published between 2010 and 2024. Manual evaluation confirmed > 99% accuracy in metadata extraction (chemical shifts, multiplicities, J-couplings, solvents) and 98% correctness in molecular-skeleton assignment; internal consistency tests yielded mean absolute errors of 0.026 ppm for <sup>1</sup>H and 0.206 ppm for <sup>13</sup>C chemical shifts. NMRexp surpasses existing public NMR databases by over an order of magnitude in size, while providing rich spectral annotations and source-DOI traceability. We anticipate that NMRexp will accelerate the development of next-generation data-driven spectral prediction, automated structure elucidation, and related AI-enabled applications.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2025-12-18
DOI
https://doi.org/10.1038/s41597-025-06245-5
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

NMRexp: A database of 3.3 million experimental NMR spectra

Yongfeng Jin, Xiaohong Ji, Xi Fang, Rong Zhu et al.
Scientific Data
Machine Learning in Materials Science
article

NMRexp: A database of 3.3 million experimental NMR spectra

Yongfeng Jin, Xiaohong Ji, Xi Fang, Rong Zhu, Xingxu Huang, Guolin Ke, Haoyi Tao, E Weinan, Yujie Liu, Linfeng Zhang, Chunyi Zhi, Fanjie Xu, Junjie Wang
article en

Abstract

Although NMR spectroscopy is one of the most fundamental methods for structural characterization in organic chemistry, high-quality and large-scale experimental NMR datasets remain a critical bottleneck for training robust, generalizable spectral-prediction and structure-elucidation models. Here, we introduce NMRexp, an open, curated collection of 3.3 million experimental NMR records for six nuclei (<sup>1</sup>H, <sup>13</sup>C, <sup>19</sup>F, <sup>31</sup>P, <sup>29</sup>Si, <sup>11</sup>B), extracted from nearly 200 thousand Supporting Information documents published between 2010 and 2024. Manual evaluation confirmed > 99% accuracy in metadata extraction (chemical shifts, multiplicities, J-couplings, solvents) and 98% correctness in molecular-skeleton assignment; internal consistency tests yielded mean absolute errors of 0.026 ppm for <sup>1</sup>H and 0.206 ppm for <sup>13</sup>C chemical shifts. NMRexp surpasses existing public NMR databases by over an order of magnitude in size, while providing rich spectral annotations and source-DOI traceability. We anticipate that NMRexp will accelerate the development of next-generation data-driven spectral prediction, automated structure elucidation, and related AI-enabled applications.

Scientific DataVol. 12(1)
Xiamen University (CN), Peking University (CN), ShanghaiTech University (CN), Chinese Institute for Brain Research (CN), Beijing National Laboratory for Molecular Sciences (CN)
National Natural Science Foundation of China, Peking University, National Science and Technology Major Project, Beijing National Laboratory for Molecular Sciences
Openalex Percentile: Top 66%
Machine Learning in Materials Science
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.