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.