Data-driven prediction of polymer surface adhesion using high-throughput MD and hybrid network models

Designing of functional polymers with tailored applications requires an understanding of their interactions with surfaces that find applications in chemical, biological and industrial processes. This work involves computing and predicting the adhesive free energies of generic, coarse-grained polymers to generic functionalized surfaces having variable sequences, composition, spatial patterns and polymer-surface interaction strengths. Enhanced molecular dynamics simulations (approximately 0.85 million simulations) are employed to generate a unique synthetic dataset of 8464 polymer-surface binding free energies (BFEs). The computed BFEs account for the effects of polymer sequences, polymer/surface composition and variable polymer-surface interaction strengths. This dataset is used for training the CNN-GRU attention-based hybrid model that accurately predicts the BFEs as well as complete free energy profiles of the polymers binding to surfaces with unknown sequences and composition. Our work provides a unified approach using MD simulations with ML to accelerate the design of functional polymers for various interfacial applications.