Intelligent understanding of spectra: from structural elucidation to property design.

Journal: Chemical Society reviews
Published Date: Aug 20, 2025

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.

Authors

  • Shuo Feng
    Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, China.
  • Meng Huang
    State Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, China. jiangj1@ustc.edu.cn.
  • Yanbo Li
    Department of Toxicology and Hygienic Chemistry, School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, China.
  • Aoran Cai
    Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, China.
  • Xiaoyu Yue
    State Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, China. jiangj1@ustc.edu.cn.
  • Song Wang
    Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, China.
  • Linjiang Chen
    Department of Ophthalmology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Jun Jiang
    Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, China.
  • Yi Luo
    Electrical and Computer Engineering Department, Bioengineering Department, University of California, Los Angeles, CA 90095 USA, and also with the California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA.

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