  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.
