Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures

Area of Science:

• Green Chemistry
• Computational Chemistry
• Materials Science

Background:

• Deep eutectic solvents (DES) are recognized as sustainable alternatives in industrial applications.
• A vast number of DES remain uncharacterized, necessitating efficient predictive tools.
• Accurate density prediction is crucial for the industrial scale-up and application of DES.

Purpose of the Study:

• To develop and validate quantitative structure-property relationship (QSPR) models for predicting DES density.
• To identify key structural features influencing DES density.
• To create an enhanced predictive model using consensus prediction.

Main Methods:

• Development of QSPR models using a validated dataset.
• External validation using recently reported experimental DES density data.
• Application of intelligent consensus prediction for improved accuracy.

Main Results:

• Robust and sound QSPR models were established for DES density estimation.
• Models demonstrated strong performance on an external validation set.
• Key structural descriptors influencing DES density were identified.
• A consensus model achieved improved predictive accuracy.

Conclusions:

• Validated QSPR models provide an efficient method for profiling DES density.
• The developed models can guide the rational design of DES for specific applications.
• Future work will focus on modeling other thermodynamic properties of DES.