Abstract：
This study addresses antibiotic pollution in global water bodies by integrating machine learning and optimization algorithms to develop a novel reverse synthesis strategy for inorganic catalysts. We meticulously analyzed data from 96 studies, ensuring quality through preprocessing steps. Employing the AdaBoost model, we achieved 90.57% accuracy in classification and an R²value of 0.93 in regression, showcasing strong predictive power. A key innovation is the Sparrow Search Algorithm (SSA), which optimizes catalyst selection and experimental setup tailored to specific antibiotics. Empirical experiments validated SSA\'s efficacy, with degradation rates of 94% for Levofloxacin and 97% for Norfloxacin, aligning closely with predictions within a 2% margin of error. This research advances theoretical understanding and offers practical applications in material science and environmental engineering, significantly enhancing catalyst design efficiency and accuracy through the fusion of advanced machine learning techniques and optimization algorithms.
摘要：
本研究通过整合机器学习和优化算法来开发一种新型的无机催化剂反向合成策略，解决了全球水体中的抗生素污染问题。我们认真分析了96项研究的数据，通过预处理步骤保证质量。采用AdaBoost模型，我们在分类中达到了90.57%的准确率，在回归中达到了0.93的R²值，展示了强大的预测能力。一个关键的创新是麻雀搜索算法(SSA)，这优化了催化剂选择和针对特定抗生素定制的实验设置。经验实验验证了SSA的疗效，左氧氟沙星降解率为94%，诺氟沙星降解率为97%，在2%的误差范围内与预测紧密结合。这项研究促进了理论理解，并在材料科学和环境工程中提供了实际应用，通过融合先进的机器学习技术和优化算法，显著提高催化剂设计效率和准确性。
