  Spectroscopic techniques are essential tools for determining the structure of
molecules. Different spectroscopic techniques, such as Nuclear magnetic
resonance (NMR), Infrared spectroscopy, and Mass Spectrometry, provide insight
into the molecular structure, including the presence or absence of functional
groups. Chemists leverage the complementary nature of the different methods to
their advantage. However, the lack of a comprehensive multimodal dataset,
containing spectra from a variety of spectroscopic techniques, has limited
machine-learning approaches mostly to single-modality tasks for predicting
molecular structures from spectra. Here we introduce a dataset comprising
simulated $^1$H-NMR, $^{13}$C-NMR, HSQC-NMR, Infrared, and Mass spectra
(positive and negative ion modes) for 790k molecules extracted from chemical
reactions in patent data. This dataset enables the development of foundation
models for integrating information from multiple spectroscopic modalities,
emulating the approach employed by human experts. Additionally, we provide
benchmarks for evaluating single-modality tasks such as structure elucidation,
predicting the spectra for a target molecule, and functional group predictions.
This dataset has the potential automate structure elucidation, streamlining the
molecular discovery pipeline from synthesis to structure determination. The
dataset and code for the benchmarks can be found at
https://rxn4chemistry.github.io/multimodal-spectroscopic-dataset.
