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Surface tension is a critical property that influences polymer behavior at interfaces and affects applications ranging from coatings to biomedical devices. Traditional experimental methods for measuring polymer surface tension are time-consuming, costly, and sensitive to environmental conditions. Computational approaches such as molecular dynamics (MD) simulations are valuable but computationally intensive, especially for polymers with long chains. This study investigates the use of machine learning (ML) techniques to predict polymer surface tension using different levels of molecular representation, focusing on multilinear regression (MLR), random forest (RF), and graph neural networks (GNNs). A data set of 317 homopolymers collected from the PolyInfo database is used to train and evaluate these models. Descriptors are derived at various levels of complexity, ranging from manually calculated features to graph-based representations. The GNN approach captures the intrinsic connectivity of polymer structures, while the MLR and RF models rely on manually crafted descriptors. The performance of these models is compared with experimental data, with the GNN model demonstrating superior accuracy due to its ability to directly learn from molecular graphs. Our results show that GNNs can better capture complex nonlinear relationships in polymer structures than traditional descriptorbased methods, suggesting their significant potential for accelerating polymer design and development. The study also includes validation of model predictions against molecular dynamics simulations, highlighting the potential of GNNs to accurately model polymer interfacial properties. 

This study investigates the role of chain architecture and block asymmetry on the morphology of AB2 miktoarm star block copolymers (AB2 BCPs) in the strongly segregated regime using molecular dynamics simulations. Notably, the cylindrical morphology in AB2 BCPs persists across a broad compositional range, extending close to fA ≈ 0.5, in agreement with both theoretical and experimental findings. The lamellar morphology observed up to fA ≈ 0.8 matches predictions; however, beyond this point, AB2 BCPs continue to exhibit lamellar structures (disk-like micelles), deviating from the expected transitions to cylindrical or spherical morphologies. This behavior, corroborated by dissipative particle dynamics simulations, is attributed to the B arms’ preference to occupying the outer regions of curved interfaces, which hinders the formation of cylindrical or spherical morphologies. Furthermore, domain spacing results exhibit remarkable agreement with strong-stretching theory (SST) across different morphologies, reinforcing the predictive power of SST. Finally, shape parameter analysis, including metrics like asphericity and acylindricity, underscores the significant impact of chain architecture on these morphological transitions. These findings provide molecular-level insights into how chain architecture and block asymmetry dictate phase behavior and morphological stability in linear and miktoarm BCPs.