A formula-free transformer with fuzzy MoE aggregation and contrastive alignment improves IR-to-SMILES elucidation, reaching 31.8% Top-1 on experimental NIST spectra, but the >10-point gains occur only on the QM9S subset.
Transformer-based models for predicting molecular structures from infrared spectra using patch-based self-attention.The Journal of Physical Chemistry A, 129(8):2077–2085, 2025
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Data Fusion and Contrastive Alignment for Unconstrained IR Molecular Structure Elucidation
A formula-free transformer with fuzzy MoE aggregation and contrastive alignment improves IR-to-SMILES elucidation, reaching 31.8% Top-1 on experimental NIST spectra, but the >10-point gains occur only on the QM9S subset.