应用化学2025，Vol.42Issue(5)：675-683,中插1-中插2,11.DOI:10.19894/j.issn.1000-0518.240326
基于改进遗传算法优化化学反应条件
Optimizing Chemical Reaction Conditions Based on Improved Genetic Algorithms
摘要
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.关键词
反应条件/优化/改进/遗传算法/变异Key words
Reaction conditions/Optimization/Improvement/Genetic algorithm/Mutation分类
化学化工引用本文复制引用
田梦阳,刘建闽..基于改进遗传算法优化化学反应条件[J].应用化学,2025,42(5):675-683,中插1-中插2,11.基金项目
广西财经大数据重点实验室项目(No.202405)和广西研究生教育创新计划项目(No.JGY2024351)资助 Supported by Guangxi Key Laboratory of Big Data in Finance and Economics Project(No.202405)and Guangxi Graduate Education Innovation Program(No.JGY2024351) （No.202405）
