Using Transfer Learning to Leverage Prior Knowledge in the Prediction of Adhesive Free Energies between Polymers and Surfaces

Polymer-surface interactions play a significant role in many biological processes and industrial applications. In prior work, machine learning (ML) models have been applied to predict the adhesive free energy of polymer-surface interactions and aid the inverse design. However, in extending these models, one faces the problem that substantially large datasets are not readily available and ML models trained on small datasets have low accuracies.  In this work, we demonstrate a transfer learning (TL) technique with a deep neural network (DNN) to improve the accuracies of ML models trained on small datasets when a larger database from a related system is available. When compared to direct learning (DL), the shared knowledge between the transfer and source tasks improves the performances significantly on small datasets. We explore the limits of database size on accuracy and the optimal tuning of network architecture and parameters for our learning tasks.