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The efficient treatment of polymer waste is a major challenge to marine sustainability. It is useful to reveal the factors that dominate the degradability of polymer materials for developing new polymer materials in the future. In this study, we have developed a platform for evaluating the degradability of polymers based on machine learning techniques. However, the small number of available datasets on degradability and the diversity of experimental means and conditions hinder large-scale analysis. To avoid this difficulty, we have introduced RankSVM, which can learn the preference of the degradability of polymers. We have made a ranking model to evaluate the degradability of polymers, integrating three datasets on the degradability of polymers that are measured by different means and conditions. The analysis of this ranking model using a decision tree has revealed the factors that dominate the degradability of polymers.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: polymer degradation, rank-based machine learning, PoLyInfo

Date published: 2023-09-25

Publisher: Elsevier BV

Journal:

• Patterns (ISSN: 26663899) vol. 4 p. 100846-100846

Funding:

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

MDR DOI:

First published URL: https://doi.org/10.1016/j.patter.2023.100846

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Updated at: 2024-01-05 22:12:08 +0900

Published on MDR: 2023-11-10 13:30:10 +0900