Machine Learning Framework to Predict Glass Transition Temperature in Natural Deep Eutectic Solvents: A Step toward Green Functional Materials

Key Points

• The primary aim is to develop a machine learning framework to predict the glass transition temperature of natural deep eutectic solvents.
• Utilized a dataset of various natural deep eutectic solvents
• Implemented machine learning algorithms to identify key predictors of glass transition temperature
• Analyzed physicochemical properties to improve prediction accuracy
• Achieved improved predictions of glass transition temperature compared to traditional methods
• Identified specific physicochemical properties that strongly influence glass transition temperature
• Validated the model with experimental data, demonstrating high accuracy

Abstract