Article Machine learning prediction of the mechanical properties of injection-molded polypropylene through X-ray diffraction analysis

Ryo Tamura SAMURAI ORCID (National Institute for Materials ScienceROR) ; Kenji Nagata ; Keitaro Sodeyama ; Kensaku Nakamura ; Toshiki Tokuhira ; Satoshi Shibata ; Kazuki Hammura ; Hiroki Sugisawa ; Masaya Kawamura ; Teruki Tsurimoto ; Masanobu Naito ; Masahiko Demura SAMURAI ORCID (National Institute for Materials ScienceROR) ; Takashi Nakanishi SAMURAI ORCID (National Institute for Materials ScienceROR)

Machine learning prediction of the mechanical properties of injection-molded polypropylene through X-ray diffraction analysis.pdf
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Ryo Tamura, Kenji Nagata, Keitaro Sodeyama, Kensaku Nakamura, Toshiki Tokuhira, Satoshi Shibata, Kazuki Hammura, Hiroki Sugisawa, Masaya Kawamura, Teruki Tsurimoto, Masanobu Naito, Masahiko Demura, Takashi Nakanishi. Machine learning prediction of the mechanical properties of injection-molded polypropylene through X-ray diffraction analysis. Science and Technology of Advanced Materials. 2024, 25 (1), 2388016.
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(abstract)

Predicting the mechanical properties of polymer materials using machine learning is essential for the design of next-generation of polymers. However, the strong relationship between the higher-order structure of polymers and their mechanical properties hinders the mechanical property predictions based on their primary structures. To incorporate information on higher-order structures into the prediction model, X-ray diffraction (XRD) can be used. This study proposes a strategy to generate appropriate descriptors from the XRD analysis of the injection-molded polypropylene samples, which were prepared under almost the same injection molding conditions. To this end, first, Bayesian spectral deconvolution is used to automatically create high-dimensional descriptors. Second, informative descriptors are selected to achieve highly accurate predictions by implementing the black-box optimization method using Ising machine. This approach was applied to custom-built polymer datasets containing data on homo- polypropylene and derived composite polymers with the addition of elastomers. Results show that reasonable accuracy of predictions for seven mechanical properties can be achieved using only XRD.

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Keyword: Polypropylene, X-ray diffraction, Bayesian spectral deconvolution, Ising machine, Machine learning

Date published: 2024-12-31

Publisher: Informa UK Limited

Journal:

  • Science and Technology of Advanced Materials (ISSN: 14686996) vol. 25 issue. 1 2388016

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Manuscript type: Publisher's version (Version of record)

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First published URL: https://doi.org/10.1080/14686996.2024.2388016

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Updated at: 2024-08-30 16:31:01 +0900

Published on MDR: 2024-08-30 16:31:01 +0900

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