Metal-organic frameworks (MOFs) such as zeolitic imidazolate framework-8 (ZIF-8) are promising nanomaterials for various applications like drug delivery and energy storage. The efficacy of ZIF-8 in these applications highly depends on its morphology, including size and shape. However, understanding and controlling morphology during synthesis is challenging due to the complex interactions among synthesis conditions such as precursor concentration and reaction temperature. Traditional trial-and-error methods for morphology optimization are inefficient and cannot effectively account for the combined effects of conditions. Machine learning (ML) offers a powerful alternative for morphology prediction, which can accelerate the reverse engineering process to better understand how synthesis conditions affect morphology. Despite recent advances, developing accurate ML models and selecting the appropriate ones for specific applications remain a challenge. This study addresses these issues by experimentally investigating how variations in synthesis conditions, such as precursor concentrations, solvent properties, and temperature, affect ZIF-8 morphology. Using experimental data, this work further built and compared three ML models: Random Forest (RF), Support Vector Regressor (SVR), and Neural Network (NN). Among these, the NN model has the best performance in terms of R-squared and mean squared errors. These ML models provide insights into how synthesis conditions affect ZIF-8, thus setting the basis for future studies aimed at optimizing conditions and guiding more efficient manufacturing strategy to expand the applications of this versatile nanomaterial.
Read full abstract