Effect of atomistic fingerprints on thermomechanical properties of epoxy-diamine thermoset shape memory polymers

A Shafe and CD Wick and AJ Peters and XY Liu and GQ Li, POLYMER, 242, 124577 (2022).

DOI: 10.1016/j.polymer.2022.124577

Shape memory polymers (SMP) have been a field of interest for researchers over the past few decades and SMPs with unique characteristics are being developed continuously. Careful, time- consuming design is required for improved materials. Polymer informatics is part of an effort to shorten the development time of polymers by using computation and data-driven approaches such as machine learning. A specific polymer can be described via "fingerprints ", which are a way to characterize molecular structures in a manner understandable by computers. In this paper, we describe combinations of nine epoxies and twenty-two hardeners with twenty fingerprints, and simulated the thermomechanical cycle with molecular dynamics code LAMMPS. Subsequently, we statistically analyzed which fingerprints are most strongly correlated with each shape memory property, specifically recovery stress and shape recovery ratio. This study lays a solid foundation for choosing and understanding atomistic fingerprints in order to discover new SMPs via machine learning.

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