  Machine learning (ML) accelerates the exploration of material properties and
their links to the structure of the underlying molecules. In previous work [J.
Shi, M. J. Quevillon, P. H. A. Valen\c{c}a, and J. K. Whitmer, \textit{ACS
Appl. Mater. Interfaces.}, 2022, 14, 32, 37161--37169], ML models were applied
to predict the adhesive free energy of polymer--surface interactions with high
accuracy from the knowledge of the sequence data, demonstrating successes in
inverse-design of polymer sequence for known surface compositions. While the
method was shown to be successful in designing polymers for a known surface,
extensive datasets were needed for each specific surface in order to train the
surrogate models. Ideally, one should be able to infer information about
similar surfaces without having to regenerate a full complement of adhesion
data for each new case. In the current work, we demonstrate a transfer learning
(TL) technique using a deep neural network to improve the accuracy of ML models
trained on small datasets by pre-training on a larger database from a related
system and fine-tuning the weights of all layers with a small amount of
additional data. The shared knowledge from the pre-trained model facilitates
the prediction accuracy significantly on small datasets. We also explore the
limits of database size on accuracy and the optimal tuning of network
architecture and parameters for our learning tasks. While applied to a
relatively simple coarse-grained (CG) polymer model, the general lessons of
this study apply to detailed modeling studies and the broader problems of
inverse materials design.
