Linking synthesis and structure descriptors from a large collection of synthetic records of zeolite materials

Koki Muraoka, Yuki Sada, Daiki Miyazaki, Watcharop Chaikittisilp () and Tatsuya Okubo ()
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Koki Muraoka: The University of Tokyo
Yuki Sada: The University of Tokyo
Daiki Miyazaki: The University of Tokyo
Watcharop Chaikittisilp: The University of Tokyo
Tatsuya Okubo: The University of Tokyo

Nature Communications, 2019, vol. 10, issue 1, 1-11

Abstract: Abstract Correlating synthesis conditions and their consequences is a significant challenge, particularly for materials formed as metastable phases via kinetically controlled pathways, such as zeolites, owing to a lack of descriptors that effectively illustrate the synthesis protocols and their corresponding results. This study analyzes the synthetic records of zeolites compiled from the literature using machine learning techniques to rationalize physicochemical, structural, and heuristic insights to their chemistry. The synthesis descriptors extracted from the machine learning models are used to identify structure descriptors with the appropriate importance. A similarity network of crystal structures based on the structure descriptors shows the formation of communities populated by synthetically similar materials, including those outside the dataset. Crossover experiments based on previously overlooked structural similarities reveal the synthesis similarity of zeolites, confirming the synthesis–structure relationship. This approach is applicable to any system to rationalize empirical knowledge, populate synthesis records, and discover novel materials.

Date: 2019
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DOI: 10.1038/s41467-019-12394-0

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