Corresponding Authors: Jiang Jun, Luo Yi
Affiliation: University of Science and Technology of China
Professor Jiang Jun’s Research Group Homepage:http://staff.ustc.edu.cn/~jiangj1
Abstract
Spectroscopy serves as a bridge connecting experimental observations with quantum mechanical principles, linking the microscopic structure of molecules to the macroscopic properties of materials. Despite its critical importance, establishing quantitative structure-property relationships from spectral data remains challenging, often requiring expensive quantum chemical calculations and expertise. The integration of Artificial Intelligence (AI) with spectroscopy offers transformative opportunities to overcome these limitations.AI models can utilize spectral data as molecular descriptors to construct predictive relationships—mapping spectral-structure and spectral-property correlations. This article presents representative advancements in the intersection of AI and spectroscopy, highlighting how these methods address challenges in spectral analysis: automatic spectral interpretation, efficient spectral prediction, and accurate performance determination based on spectral fingerprints. Beyond individual applications, the authors also demonstrate how AI can facilitate the development of a unified spectral-structure-property framework capable of predicting performance directly from spectral data. This integrated approach paves the way for spectroscopy-guided, AI-driven reverse design of functional materials. Furthermore, the authors emphasize the importance of model interpretability, which can elucidate the fundamental physical principles behind spectral-structure-property relationships. Looking ahead, the authors propose that combining large-scale AI architectures with spectral descriptors could establish universal spectral-structure-property relationships, potentially revolutionizing chemical theory.
DOI:10.1039/D4CS01293C
Link:https://doi.org/10.1039/D4CS01293C[Literature Transfer]Hu Wei & Jiang Jun & Luo Yi JACS: Application of Deep Learning in Bidirectional Conversion of Molecular Structure and Vibrational SpectraUniversity of Science and Technology of China Jiang Jun & Luo Yi JACS: Repairing Noise-Polluted Low-Frequency Vibrational Spectra Using Attention Mechanism U-NetUniversity of Science and Technology of China Jiang Jun & Luo Yi JACS: Machine Learning Spectral Quantitative Study on the Effect of Electric Field on CO2 ElectrocatalysisJiang Jun JPCL: Machine Learning-Based Multispectral Integrated Analysis of Molecular Structural FeaturesJiang Jun JPCL: Using Machine Learning to Identify Adsorption States of OER Intermediates on Single-Atom CatalystsHu Wei & Jiang Jun & Luo Yi JPCL: QMe14S: A Comprehensive and Efficient Organic Small Molecule Spectral DatasetUniversity of Science and Technology of China Jiang Jun Chem. Sci.: Spectral-Based Clustering of High-Entropy Alloy Catalysts: Enhancing Insights into Atomic Structure Utilization