The glass transition temperature <i>(Tg)</i> is one of the most important thermophysical properties of polymers. The substantial change in polymer dynamics at glass transition temperature causes a major change in physical properties, including mechanical modulus, density, specific heat, damping characteristics, dielectric properties of a polymer. The cheminformatics-based approach based on machine learning algorithms is often applied to predict the quantitative relationships between key molecular descriptors and properties of investigated polymers. In this work, we develop an innovative modeling framework by integrating cheminformatics and coarse-grained molecular dynamics (MD) to predict the glass transition temperature of a diverse set of hundred polymers. This synergistic approach provides valuable insights into the roles of key molecular parameters (i.e., cohesive interactions, chain stiffness, and branching) in influencing the glass transition temperature of polymers.<br/>

Predicting the glass transition behavior of polymers via integration of cheminformatics and molecular dynamics simulations

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.