Developing a Suitable Model for Water Uptake for Biodegradable Polymers Using Small Training Sets

Abstract EN

Prediction of the dynamic properties of water uptake across polymer libraries can accelerate polymer selection for a specific application.

We first built semiempirical 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 semiempirical 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.