Data‐Driven Approach for Rational Synthesis of Zeolites and Other Nanoporous Materials

Chapter 9 Data-Driven Approach for Rational Synthesis of Zeolites and Other Nanoporous Materials Watcharop Chaikittisilp, Watcharop ChaikittisilpSearch for more papers by this author Watcharop Chaikittisilp, Watcharop ChaikittisilpSearch for more papers by this author Book Editor(s):German Sastre, German SastreSearch for more papers by this authorFrits Daeyaert, Frits DaeyaertSearch for more papers by this author First published: 24 January 2023 https://doi.org/10.1002/9781119819783.ch9 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Abstract Synthesis of zeolites generally involves complicated, interrelated, synthetic parameters. Their formation mechanisms have not yet been fully clarified, because zeolites are formed through an intricate sequence of chemical reactions under hydrothermal conditions. As a result, the discovery of new zeolites and their property optimization for targeted applications have heavily relied on a trial-and-error approach based on chemical intuition from experts, generally by alteration of synthetic parameters. Such an exploratory search for the optimal synthetic parameters is prohibitively experimentally expensive. A key strategy to overcome this experimental challenge is to combine experiments with data science, especially with the machine learning algorithm approach. This approach applied to the experimental data enables us to extract the most significant synthesis descriptors over complex chemical spaces with high dimension and massive entries, which is sometimes difficult for humans to handle. In particular, the pattern recognition capability of machine learning can be exceptionally effective for materials that are synthesized through kinetically controlled pathways, such as zeolites, which are difficult to treat by straightforward computational methodologies. This chapter describes the application of machine learning techniques to analyze the synthetic records of zeolites previously reported in the literature, to extract the synthesis descriptors of zeolites. These synthesis descriptors are linked to the structure descriptors of zeolites, to rationalize the synthesis-structure relationship, and subsequently to suggest the synthesis parameters for selected zeolites. The machine learning algorithms can also be used to extract the influence of materials descriptors (i.e., physicochemical properties) on performance (e.g., adsorption capacity and catalytic activity). An example on nanoporous catalysts is explained. 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