Intelligent understanding of spectra: from structural elucidation to property design

Abstract

Spectroscopy serves as a bridge between experimental observations and quantum mechanical principles, linking molecular microstructure to macroscopic material properties. Despite its central importance, establishing quantitative structure–property relationships from spectral data remains challenging, typically requiring expensive quantum chemistry calculations and specialized expertise. The integration of artificial intelligence (AI) with spectroscopy presents a transformative opportunity to overcome these limitations. AI models can leverage spectral data as molecular descriptors to construct predictive relationships—both spectrum-to-structure and spectrum-to-property mappings. This review presents representative advances at the AI–spectroscopy intersection, highlighting how these approaches address challenges in spectroscopic analysis: automated spectral interpretation, efficient spectral prediction, and accurate property determination from spectroscopic fingerprints. Beyond individual applications, we demonstrate how AI enables the development of unified spectrum–structure–property frameworks capable of predicting functional properties directly from spectral data. This integrated approach opens pathways for spectrum-guided, AI-driven inverse design of functional matters. In addition, we emphasize the importance of model interpretability, which can illuminate the fundamental physics underlying spectrum–structure–property relationships. Looking forward, we propose that integrating large-scale AI architectures with spectroscopic descriptors could establish universal spectrum–structure–property relationships, potentially revolutionizing chemical theory.