Machine learning approaches to analyze the effect of reaction parameters on ZIF-8 synthesis

The morphology of zeolitic imidazolate framework-8 (ZIF-8) determines its effectiveness in applications like targeted drug delivery and energy storage. However, the precise control over morphology during synthesis is challenging since many reaction parameters affect it. Among these, precursor concentrations, solvents, and temperature are important parameters. As an integrative approach to experimental studies, this work develops machine learning (ML) models to predict the effects of these parameters on ZIF-8 morphology. Additionally, this work compares the performance of these models to demonstrate their potential as predictive tools for guiding the synthesis of ZIF-8 with controllable morphology.

沸石咪唑酯骨架材料 - 8（ZIF - 8）的形态决定了其在靶向药物递送和能量存储等应用中的有效性。然而，在合成过程中对形态进行精确控制具有挑战性，因为许多反应参数都会对其产生影响。在这些参数中，前驱体浓度、溶剂和温度是重要的参数。作为对实验研究的一种综合方法，本研究开发了机器学习（ML）模型来预测这些参数对ZIF - 8形态的影响。此外，本研究还对这些模型的性能进行了比较，以证明它们作为预测工具在指导合成具有可控形态的ZIF - 8方面的潜力。