Machine learning analysis of a large set of homopolymers to predict glass transition temperatures.

ABSTRACT: Glass transition temperature of polymers, Tg, is an important thermophysical property, which sometimes can be difficult to measure experimentally. In this regard, data-driven machine learning approaches are important alternatives to assess Tg values, in a high-throughput way. In this study, a large dataset of more than 900 polymers with reported glass transition temperature (T_g) was assembled from various public sources in order to develop a predictive model depicting the structure-property relationships. The collected dataset was curated, explored via cluster analysis, and then split into training and test sets for validation purposes and then polymer structures characterized by molecular descriptors. To find the models, several machine learning techniques, including multiple l

SUBMITTER: Casanola-Martin GM 

PROVIDER: S-EPMC11447034 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature