Linking synthesis and structure descriptors from a large collection of synthetic records of zeolite materials
| Autor: | Tatsuya Okubo, Watcharop Chaikittisilp, Daiki Miyazaki, Koki Muraoka, Yuki Sada |
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| Rok vydání: | 2019 |
| Předmět: |
Science
Crossover General Physics and Astronomy 02 engineering and technology 010402 general chemistry computer.software_genre 01 natural sciences General Biochemistry Genetics and Molecular Biology Article Similarity (network science) Porous materials Zeolite lcsh:Science Structure (mathematical logic) Multidisciplinary Heuristic General Chemistry 021001 nanoscience & nanotechnology 0104 chemical sciences lcsh:Q Materials chemistry Data mining 0210 nano-technology computer Inorganic chemistry |
| Zdroj: | Nature Communications Nature Communications, Vol 10, Iss 1, Pp 1-11 (2019) |
| ISSN: | 2041-1723 |
| Popis: | 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. Understanding zeolite synthesis-structure relationships remains challenging owing to the number of variables involved in their preparation. Here the authors analyze zeolite synthetic records from the literature via machine learning and find communities of synthetically related materials with previously overlooked similarities. |
| Databáze: | OpenAIRE |
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