Advancing Polyhydroxyalkanoate Biopolymer Material Design: Integrating Machine Learning and Experimental Validation

Virtually every consumer product available on the market today contains some form of fossil fuel-based polymer. However, these materials pose environmental, human health, and economic concerns due to their enduring presence in the global ecosystem and their degradation products. Addressing this crisis necessitates scalable production of biodegradable alternatives, such as polyhydroxyalkanoates (PHAs). PHAs are presented as promising substitutes due to their biodegradability, biocompatibility, and the potential for complete renewable utilization post-degradation, but a current challenge to widespread use of these materials lies in understanding the quantitative relationship between the structural characteristics of PHAs, their environmental interactions, and their degradation rates to enhance their industrial production and distribution. To bridge this knowledge gap, the dissertation outlines a comprehensive approach involving the development of a specialized dataset, the application of machine learning (ML) models to predict degradation rates based on structural and environmental factors, and the experimental validation of these predictions. The first part of this research focuses on assembling a manually curated dataset from the extensive, available open-access literature, aimed at understanding the effects of structural and environmental features on PHA degradation. The second part leverages this dataset through ML modeling, employing techniques like random forest regression to predict degradation profiles with over 80% accuracy. This methodology enables a deeper understanding of the complex interplay between chemical structures and degradation properties, surpassing traditional trial-and-error approaches. The final part of this research aims to complete an iterative workflow for dataset development by validating ML model predictions through physical experiments, enriching the original dataset with comprehensive experimental data on PHA degradation in hydrolytic environments with contact angle, molecular weight, and thermal property characterizations. The incorporation of experimental findings into the ML dataset, particularly through expanded ML techniques that emphasize pairwise feature importance such as explainable boosting machines (EBM), helps in pinpointing critical factors influencing PHA degradation, such as environmental temperature and material properties. The model performances indicate a strong performance of manually assembled literature-based datasets when predicting degradation rate for PHAs. In conclusion, a data science-based framework has been developed for exploring PHA biopolyester degradation and explores the combination of features of the material and its environment that integrates the structure, properties, and experimentally verified degradation profiles of the material. This workflow will be a useful and generalizable pipeline for PHAs and other polymers to expand the biopolymer design space with degradation in mind.