Feature vectorization of microphase-separated structures in polymeric materials using dissipative particle dynamics and persistent homology for machine learning applications

Abstract

Recently, materials informatics (MI) has gained attention as an efficient approach for materials development. However, its application to polymers has been limited owing to the complexity and significance of the higher-order structures unique to these materials. This study focuses on microphase-separated structures, among the higher-order structures, as they influence many functional polymeric materials that support modern society. To implement MI that accounts for specific higher-order structures, such as microphase-separated structures, these structures must be quantified and converted into features. This approach addresses a gap in current materials informatics, in which traditional methods do not adequately account for the complex structures of polymers. Persistent homology (PH), a topological data analysis method, was used to extract features from the microphase-separated structures of polymeric materials. A coarse-grained simulation method known as dissipative particle dynamics (DPD) was used to generate the microphase-separated structures for PH analysis. The method was validated using electrolyte membranes for fuel cells, where microphase-separated structures are critical. Topological feature extraction was successfully performed on Nafion™ and its analogs, Aquivion® and Flemion™. Additionally, the correlation between the extracted features and proton conductivity was analyzed using unsupervised machine learning, which indicated that these features can be used to predict proton conductivity. The combination of DPD and PH can effectively convert microphase-separated structures into features. This method may be applicable to a wide range of polymeric materials influenced by microphase-separated structures, as it is not limited to proton exchange membranes or proton conductivity. This research marks a significant step toward advancing polymer informatics by incorporating the microphase-separated structures of polymers.