Intelligent understanding of spectra: from structural elucidation to property design

Area of Science:

• Interdisciplinary science merging spectroscopy, quantum mechanics, and artificial intelligence.
• Materials science and chemistry focused on structure-property relationships.

Background:

• Spectroscopy links molecular structure to material properties but quantitative analysis is challenging.
• Traditional methods require expensive quantum chemistry calculations and specialized expertise.
• Artificial intelligence (AI) offers a novel approach to interpret spectral data.

Purpose of the Study:

• To review advances in integrating AI with spectroscopy for enhanced analysis.
• To demonstrate AI's capability in establishing spectrum-structure-property relationships.
• To explore AI-driven inverse design of functional materials guided by spectral data.

Main Methods:

• Leveraging AI models to use spectral data as molecular descriptors.
• Developing predictive models for spectrum-to-structure and spectrum-to-property mappings.
• Constructing unified spectrum-structure-property frameworks.

Main Results:

• AI enables automated spectral interpretation, efficient spectral prediction, and accurate property determination.
• Unified AI frameworks predict functional properties directly from spectroscopic fingerprints.
• AI enhances the understanding of fundamental physics governing spectrum-structure-property relationships.

Conclusions:

• AI-spectroscopy integration overcomes limitations in traditional spectral analysis.
• This approach enables spectrum-guided inverse design of materials.
• Future large-scale AI architectures could establish universal spectrum-structure-property relationships, revolutionizing chemical theory.