Multitask Neural Network for Mapping the Glass Transition and Melting Temperature Space of Homo- and Co-Polyhydroxyalkanoates Using σProfiles Molecular Inputs

Abstract

Polyhydroxyalkanoates (PHAs) are an emerging type of bioplastic that have the potential to replace petroleum-based plastics. They are biosynthetizable, biodegradable, and economically viable and have a range of tunable properties. Despite their great potential, the structure and properties of PHA remain unexplored due to their theoretically infinite chemical space. Therefore, computational approaches for accurate predictions of their various properties need to be developed to effectively explore this large chemical space. For this purpose, this work presents a multitask artificial neural network (ANN) capable of predicting the glass transition temperature (Tg) and melting temperature (Tm) of PHA homopolymers and copolymers. The ANN inputs included the σProfiles as molecular parameters describing the monomer chemistry and their composition. In contrast, the polymer molecular weight (M) and polydispersity index (PDI) were used to describe the polymer state. The results showed that after optimizing the hyperparameters, the selected ANN architecture was remarkable in predicting the Tg and Tm of PHA with R2 values of 0.979 and 0.986 and average absolute relative deviation (AARD) of 0.476% and 0.520%, respectively. The proposed model represents an initiative to promote the development of robust, open source, and user-friendly models capable of predicting the properties of polymers based solely on molecular parameters (σProfiles), thereby saving time and resources for researchers worldwide. The framework described in this work is flexible so that it can be applied to a larger chemical space and incorporate other properties of polymers.