ArticleDigital discovery2025
Setting new benchmarks in AI-driven infrared structure elucidation.
Article in Digital discovery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The trial behind it
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Who cites it
4 citing papers in PubMed.
- Vib2Mol: from vibrational spectra to molecular structures-a unified deep learning framework.Chemical science · 2026Article
- Automatic identification of compounds in molecular mixtures from liquid-phase infrared spectra.Chemical science · 2026Article
- Towards automatically verifying chemical structures: the powerful combination ofChemical science · 2025Article
- From Spectra to Structure: AI-PoweredAnalytical chemistry · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automated structure elucidation from infrared (IR) spectra represents a significant breakthrough in analytical chemistry, having recently gained momentum through the application of Transformer-based language models. In this work, we improve our original Transformer architecture, refine spectral data representations, and implement novel augmentation and decoding strategies to significantly increase performance. We report a Top-1 accuracy of 63.79% and a Top-10 accuracy of 83.95% compared to the current performance of state-of-the-art models of 53.56% and 80.36%, respectively. Our findings not only set a new performance benchmark but also strengthen confidence in the promising future of AI-driven IR spectroscopy as a practical and powerful tool for structure elucidation. To facilitate broad adoption among chemical laboratories and domain experts, we openly share our models and code.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.