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
- Yongfeng Jin (ORCID: https://orcid.org/0000-0001-7125-7055)
- Xiaohong Ji (ORCID: https://orcid.org/0000-0001-8342-7357)
- Xi Fang (ORCID: https://orcid.org/0000-0001-8938-1732)
- Rong Zhu (ORCID: https://orcid.org/0000-0001-5035-3531)
- Xingxu Huang (ORCID: https://orcid.org/0000-0001-8934-1247)
- Guolin Ke (ORCID: https://orcid.org/0000-0002-1227-7221)
- Haoyi Tao
- E Weinan
- Yujie Liu (ORCID: https://orcid.org/0000-0003-4328-4069)
- Linfeng Zhang (ORCID: https://orcid.org/0000-0002-0079-0570)
- Chunyi Zhi (ORCID: https://orcid.org/0000-0001-6766-5953)
- Fanjie Xu
- Junjie Wang (ORCID: https://orcid.org/0009-0001-9328-5863)
Institutions
- Xiamen University (CN)
- Peking University (CN)
- ShanghaiTech University (CN)
- Chinese Institute for Brain Research (CN)
- Beijing National Laboratory for Molecular Sciences (CN)
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
- National Natural Science Foundation of China
- Peking University
- National Science and Technology Major Project
- Beijing National Laboratory for Molecular Sciences