(149a) High-Throughput Measurement of a Machine Learning Model for Polyester Biodegradation
AIChE Annual Meeting
Monday, November 8, 2021 - 12:30pm to 12:50pm
Here, we combine high throughput synthesis, high throughput biodegradation assays, and machine learning to produce a predictive model for polyester biodegradability. This study utilized the clear zone test to obtain biodegradation rates of hundreds of polyesters using a sample of different bacterial strains. For the clear zone test, polymer is homogeneously dispersed in media, and its degradation during bacterial growth is monitored optically. This biodegradation test was applied to a library of hundreds of polyesters representing diverse synthetic routes and monomer functionalities, including biobased and synthetic systems to generate a large data set for machine learning. Different representations for polymer chemical structure and machine learning algorithms were explored in order to identify key chemical features that promote degradation and to identify models with the greatest predictive capacity.