Project B2

The goal of B2 is to move towards a systematic and predictive approach for polyester for biodegradation, integrating a deeper understanding of the combination of material parameters that affect the hydrolysis rate of polyesters, along with predictive models and experimental data. It is aimed to create a dataset that integrates both experimental and theoretical research on polyesters. This dataset will be used to train a machine-learning model with an aim to predict the optimal comonomers necessary to achieve a balance in the glass transition temperature, crystallinity, and structural properties of polyesters. This balance is crucial for enhancing hydrolysis which, in turn, facilitates biodegradation. The dataset will encompass various homo- and co-polyesters with systematic variation in the molecular structure as well as corresponding properties.