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# Molecular Structure Elucidation

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Journal of the American Chemical Society Nov 11, 2026
NMR spectra now yield molecular skeletons directly: inverse-IMPRESSION solves 11 of 19 experimental molecules
inverse-IMPRESSION maps NMR spectra directly onto bond probabilities between atoms, assembling candidate structures through one-shot prediction, stepwise bond filling and noise-augmented multishot sampling. Where comparable graph methods drop sharply on experimental spectra, the platform reaches 77.8% Top-1 on simulated data using only hydrogen and carbon NMR information and correctly identifies 11 of 19 experimental molecules, including a polycyclic steroid of 480.7 Da with an nSPS of 48.1.
inverse-IMPRESSION maps NMR spectra directly onto bond probabilities between atoms, assembling candidate structures through one-shot prediction, stepwise bond filling and noise-augmented multishot sampling. Where comparable graph methods drop sharply on experimental spectra, the platform reaches 77.8% Top-1 on simulated data using only hydrogen and carbon NMR information and correctly identifies 11 of 19 experimental molecules, including a polycyclic steroid of 480.7 Da with an nSPS of 48.1.
University of Bristol
Zheqi Jin（First author）Craig Butts（Corresponding author）
inverse-IMPRESSION platform Molecular Structure Elucidation Graph Transformer IMPRESSION-G2 Nuclear Magnetic Resonance Spectroscopy
arXiv Nov 12, 2026
Vibrational spectra skip per-point DFT: HARMONY anchors on harmonic spectra and returns experimental IR and Raman in 2.12 seconds per molecule
Predicting experimental vibrational spectra from molecular structure has been caught between theory and measurement: quantum chemistry is accurate but slow, while end-to-end neural networks are fast but bypass explicit vibrational information. HARMONY assembles harmonic spectra from atomic forces and spectroscopic tensors predicted directly by equivariant neural networks, then uses HarmoNet to map them to experimental IR and Raman spectra conditioned on molecular topology and measurement metadata. On the HarmoSpec test set, the full workflow reaches mean cosine similarities of 0.912 for infrared and 0.907 for Raman, with a mean of 2.12 seconds per molecule, roughly 600-fold faster than the evaluated DFT workflow.
Predicting experimental vibrational spectra from molecular structure has been caught between theory and measurement: quantum chemistry is accurate but slow, while end-to-end neural networks are fast but bypass explicit vibrational information. HARMONY assembles harmonic spectra from atomic forces and spectroscopic tensors predicted directly by equivariant neural networks, then uses HarmoNet to map them to experimental IR and Raman spectra conditioned on molecular topology and measurement metadata. On the HarmoSpec test set, the full workflow reaches mean cosine similarities of 0.912 for infrared and 0.907 for Raman, with a mean of 2.12 seconds per molecule, roughly 600-fold faster than the evaluated DFT workflow.
Xiamen University Shanghai Innovation Institute East China Normal University Shanghai Artificial Intelligence Laboratory
Xinyu Lu（First author）Hao Ma（Corresponding author）Bin Ren（Corresponding author）Tong Zhu（Corresponding author）
HARMONY HarmoNet HarmoSpec Experimental Vibrational Spectrum Prediction Equivariant Neural Networks

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