Abstract:

The optimization of organic chemical reaction conditions has always been an important research topic in the field of chemistry. However， due to the diversity and complexity of reaction conditions， traditional optimization methods often rely on a large number of experiments， facing challenges such as high costs and long reaction times. This study proposes an improved genetic algorithm optimization model addressing the characteristics of organic chemical reaction optimization and the shortcomings of traditional genetic algorithms in terms of convergence speed and local optima issues. The model combines an elitism strategy， adaptive multiple mutation strategy， and random selection strategy， significantly improving the algorithm's global search capability and convergence speed. The study first evaluates the model on a dataset of direct arylation reactions containing mixed-type conditions， showing that the improved genetic algorithm exhibits stronger optimization ability and higher stability compared to traditional genetic algorithms and random search algorithms. Subsequently， the study optimizes the Suzuki-Miyaura reaction using a dataset containing 3696 reaction conditions. The experimental results demonstrate that when reaction conditions with a yield of at least 96.20% account for only 1% of the entire search space， the improved genetic algorithm requires an average of only 35 samples to find the optimal reaction conditions that meet this criterion， fully demonstrating the immense potential of the improved genetic algorithm in optimizing chemical reaction conditions.

Key words: Reaction conditions, Optimization, Improvement, Genetic algorithm, Mutation