Résumé
Converting atomic layer deposited (ALD) ZnO thin films into high-quality zeolitic imidazolate framework-8 (ZIF-8) membranes poses significant challenges in identifying optimal synthesis conditions. This study employs a comprehensive machine learning approach to predict conversion outcomes based on 68 experimental conditions with varying solvent systems, temperatures, and reaction durations. We systematically evaluated 7 classification algorithms including k-nearest neighbors (k-NN), random forests, neural networks, and decision trees using stratified 10-fold cross-validation. The optimized k-NN classifier (k=5) achieved 92.6% accuracy with a Kappa statistic of 0.791, demonstrating excellent discrimination between high- and low-quality membrane layer outcomes. Feature importance analysis identified the primary solvent as the most influential predictor, followed by temperature and reaction duration within specific2regimes. Decision tree analysis further revealed a critical temperature threshold of 80°C for methanol-based systems, below which extended reaction times are required. Application of Synthetic Minority Over-sampling Technique (SMOTE) improved minority class detection while maintaining high specificity. The developed predictive framework enables the screening of conversion conditions with over 90% confidence, potentially reducing the number of experimental trials significantly while accelerating the discovery and optimization of ZIF-8 membrane fabrication protocols. This data-driven methodology provides a blueprint for extending machine learning-based optimization to other metal-organic framework systems and complex materials synthesis challenges.