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arXiv · 2110.03041

Predicting Adhesive Free Energies of Polymer--Surface Interactions with Machine Learning

Jiale Shi·Michael J. Quevillon·Pedro H. Amorim Valença·Jonathan K. Whitmer
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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 $20,000$ 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.

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Jiale Shi, Michael J. Quevillon, Pedro H. Amorim Valença, Jonathan K. Whitmer. 2021-10-06. Predicting Adhesive Free Energies of Polymer--Surface Interactions with Machine Learning. https://arxiv.org/abs/2110.03041

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