Deep Set Model for the Automated NMR Fingerprinting of Unknown Mixtures

Key Points

• The aim is to develop a deep learning model for identifying components in unknown mixtures using NMR spectroscopy.
• Developed a deep set model for analyzing NMR data
• Applied the model to various unknown mixtures of compounds
• Compared results with traditional methods of mixture analysis
• The deep set model showed improved accuracy in identifying components compared to standard techniques
• Automated approach significantly reduced analysis time
• Results indicate enhanced capability in handling complex mixtures