Predicting Adhesive Free Energies of Polymer--Surface Interactions with Machine Learning
Abstract
Polymer-surface interactions are crucial to many biological processes and industrial applications. Here we propose a machine-learning method to connect a model polymer's sequence with its adhesion to decorated surfaces. We simulate the adhesive free energies of unique coarse-grained 1D sequential polymers interacting with functionalized surfaces and build support vector regression (SVR) models that demonstrate inexpensive and reliable prediction of the adhesive free energy as a function of the sequence. Our work highlights the promising integration of coarse-grained simulation with data-driven machine learning methods for the design of new functional polymers and represents an important step toward linking polymer compositions with polymer-surface interactions.
Keywords
Cite
@article{arxiv.2110.03041,
title = {Predicting Adhesive Free Energies of Polymer--Surface Interactions with Machine Learning},
author = {Jiale Shi and Michael J. Quevillon and Pedro H. Amorim Valença and Jonathan K. Whitmer},
journal= {arXiv preprint arXiv:2110.03041},
year = {2021}
}