Revealing factors influencing polymer degradation with rank-based machine learning

Weilin Yuan; Yusuke Hibi; Ryo Tamura; Masato Sumita; Yasuyuki Nakamura; Masanobu Naito; Koji Tsuda

doi:10.1016/j.patter.2023.100846

Revealing factors influencing polymer degradation with rank-based machine learning

Patterns (N Y). 2023 Sep 25;4(12):100846. doi: 10.1016/j.patter.2023.100846. eCollection 2023 Dec 8.

Authors

Weilin Yuan¹, Yusuke Hibi², Ryo Tamura^{1

3

4}, Masato Sumita⁴, Yasuyuki Nakamura², Masanobu Naito², Koji Tsuda^{1

3

4}

Affiliations

¹ Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan.
² Research Center for Macromolecules and Biomaterials, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan.
³ Center for Basic Research on Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan.
⁴ RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan.

Abstract

The efficient treatment of polymer waste is a major challenge for marine sustainability. It is useful to reveal the factors that dominate the degradability of polymer materials for developing polymer materials in the future. The small number of available datasets on degradability and the diversity of their experimental means and conditions hinder large-scale analysis. In this study, we have developed a platform for evaluating the degradability of polymers that is suitable for such data, using a rank-based machine learning technique based on RankSVM. We then 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. Analysis of this ranking model with a decision tree revealed factors that dominate the degradability of polymers.

Keywords: RankSVM; data integration; degradability factors analyzation; exposure experiments; marine sustainability; polymer degradability prediction; rank-based machine learning.