Benchmarking Predictive Models: Empirical vs. ML for Solubility of Olive-Derived Phenolic Compounds in Supercritical Media

Key Points

• Findings indicate that machine learning models outperform traditional empirical methods in predicting solubility.
• Machine learning models achieved up to 90% accuracy in predicting solubility under various conditions.
• Empirical and machine learning approaches were benchmarked against each other using a dataset of phenolic compounds.
• This research highlights the potential of machine learning to enhance predictive capabilities in chemical solubility studies.