Download file (5.8MB)
Download as zip (5.1MB)

コレクション

引用

説明:

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.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: Polypropylene, X-ray diffraction, Bayesian spectral deconvolution, Ising machine, Machine learning

刊行年月日: 2024-12-31

出版者: Informa UK Limited

掲載誌:

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

研究助成金:

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1080/14686996.2024.2388016

関連資料:

その他の識別子:

連絡先:

更新時刻: 2024-08-30 16:31:01 +0900

MDRでの公開時刻: 2024-08-30 16:31:01 +0900