title: 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 semiempirical models using Artificial Neural Networks and all water uptake data, as individual input. These models give very good correlations (R (2) > 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 (R (2) = 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.
authors.affiliation: Department of Chemical and Bioprocess Engineering, Research Center for Nanotechnology and Advanced Materials "CIEN-UC", Pontificia Universidad Católica de Chile, Vicuña Mackenna 2860, Macul, 7820436 Santiago, Chile.
authors.forename: Loreto M
authors.initials: LM
authors.lastname: Valenzuela
authors.affiliation: Department of Mechanical and Aerospace Engineering, Rutgers, The State University of New Jersey, New Brunswick, NJ 08854-8087, USA.
authors.forename: Doyle D
authors.initials: DD
authors.lastname: Knight
authors.affiliation: New Jersey Center for Biomaterials, Rutgers, The State University of New Jersey, 145 Bevier Road, Piscataway, NJ 08854, USA.
authors.forename: Joachim
authors.initials: J
authors.lastname: Kohn