Hypothesis-and-Refinement Learning of Organic Structures from Multimodal Spectroscopic Data
Determining molecular structures from spectroscopic data remains fundamentally challenging because the inverse problem is intrinsically underdetermined: individual spectra are sparse, low-dimensional, and encode only partial structural evidence relative to the vast space of possible molecules. We address this challenge by formulating automated structure elucidation as a scalable hypothesis-refinement paradigm that tightly integrates spectral evidence with large-scale molecular priors. To supply structure-resolving NMR signals for multimodal learning, we construct \textbf{QM9SPIN}, a DFT-derived dataset comprising diverse 1D and 2D spectra, including J-coupling, DEPT experiments, and expli...
| Authors | Chengchun Liu, Zhiyuan Yan, Li Yuan et al. |
|---|---|
| Primary category | physics.chem-ph |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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