A new mapping/exploration approach for HT synthesis of zeolites

This work shows a methodology for the synthesis of self-assembled organic-inorganic materials which integrates high-throughput tools for the synthesis and characterization of solid materials and data-mining techniques in materials science. This is illustrated by a detailed exploration of the hydrothermal synthesis in the system SiO2:GeO2:Al2O3: F-:H2O:N(16) methylsparteinium. Data analysis and dimensional reduction were conducted by using principal components analysis and clustering algorithms, allowing the definition of a new and suitable structural vector which summarizes the X-ray diffraction characterization data as well as an improvement of data visualization and interpretation. Different modeling techniques were applied for the prediction of the properties of the materials considering the synthesis descriptors as input of the model. Furthermore, different "material property" descriptors were considered as outcome of the model, that is, the crystallinity of the formed phases, structural principal components computed by principal component analysis, or clustering results. It was found that the final properties of the materials could be successfully modeled using artificial neural networks and decision trees.