Machine Learning Approach for Predicting Fluoroquinolone Adsorption on Metal-Organic Frameworks

Key Points

• Fluoroquinolone adsorption is predicted effectively by machine learning techniques, enhancing efficiency.
• Machine learning models achieved a predictive accuracy greater than 85%, indicating high reliability.
• Analysis employs robust algorithms to evaluate the interaction between fluoroquinolone and metal-organic frameworks.
• These findings highlight the potential for machine learning to optimize adsorbent materials for environmental applications.