Rational Design of High-Quality ZIF-8 Membranes: Machine Learning-Guided Optimization of ALD ZnO Conversion

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 (<i>k</i> = 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 specific regimes. 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 Oversampling 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.