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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.

Rights:

• Creative Commons BY Attribution 4.0 International
  

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

Funding:

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

MDR DOI:

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