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

Naoya Ohtsuka; Muhammad Zhafran MOHD ARIS; Toshiyasu Suzuki; Norie Momiyama

doi:10.1039/d5cp03554f

January 1, 2026Physical Chemistry Chemical PhysicsOpen Access

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

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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.

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Implication

Rational design of additives enhances halogenation efficiency in highly fluorinated naphthalenes, suggesting new methods for transformation.

Abstract

Highly fluorinated aromatic compounds exhibit unique electronic structures, however their selective transformation remains a longstanding challenge. Halogenation of F7 naphthalene previously required low temperatures (-40 to 0 °C) for high...

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Cite This Study

Ohtsuka et al. (2026) studied this question.

synapsesocial.com/papers/699a9d3c482488d673cd30d8 https://doi.org/10.1039/d5cp03554f

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

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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