Machine Learning-Guided Control over Polymer Sequence and Degradability
Iowa State University
Computation and Data Science
The degradability of bio-derived and bio-degradable polymers is often related to the sequence of monomer repeat units, which can be challenging to tune synthetically. This project will develop new simulation tools to optimize the sequence and degradability of polymers without requiring a large number of Edisonian experiments performed by trial and error. Artificial intelligence and machine learning play an integral role in this proposal to accelerate the discovery of optimal polymerization conditions that enhance degradability. Select experiments will create a feedback loop with predictions from simulations in a Materials Genome-Inspired iteration among data science, computation, synthesis, and characterization.