Developing a suitable model for water uptake for biodegradable polymers using small training sets

Abstract

Prediction of the dynamic properties of water uptake across polymer libraries can accelerate polymer selection for a specific application. We first built semi-empirical models using Artificial Neural Networks and all water uptake data, as individual input. These models give very good correlations (R2>0.78 for test set), but very low accuracy on cross-validation sets (less than 19% of experimental points within experimental error). Instead, using consolidated parameters like equilibrium water uptake a good model is obtained (R2 = 0.78 for test set), with accurate predictions for 50% of tested polymers. The semi-empirical model was applied to the 56-polymer library of L-tyrosine-derived polyarylates, identifying groups of polymers that are likely to satisfy design criteria for water uptake. This research demonstrates that a surrogate modeling effort can reduce the number of polymers that must be synthesized and characterized to identify an appropriate polymer that meets certain performance criteria.