Data-guided rational design of additives for halogenation of highly fluorinated naphthalenes: Integrating fluorine chemistry and machine learning

Key Points

• The aim is to develop a strategy to improve the halogenation of highly fluorinated naphthalenes using machine learning.
• Utilized machine learning algorithms to analyze the electronic structure of fluorinated naphthalenes.
• Identified potential additives to facilitate the halogenation process.
• Conducted experiments to test the effectiveness of these additives under various conditions.
• Successfully reduced the required reaction temperature for halogenation.
• Achieved higher selectivity and yield in the halogenation process with the optimized additives.
• Demonstrated a more efficient approach to modifying highly fluorinated compounds.