Committee:

• Dr. Rampi Ramprasad, Materials Science and Engineering, Georgia Institute of Technology (Advisor)
• Dr. Seung Soon Jang, Materials Science and Engineering, Georgia Institute of Technology
• Dr. Will Gutekunst, School of Chemistry and Biochemistry, Georgia Institute of Technology
• Dr. Aaron Stebner, Materials Science and Engineering, Georgia Institute of Technology
• Keith Hearon, CEO, Nuceptive Labs, Inc

Abstract: Addressing the global plastic waste crisis requires a new paradigm of polymeric material that can be depolymerized back to monomer, enabling true chemical recycling. Polymers synthesized via ring opening polymerization (ROP) have shown promise in the fact that they tend to have the necessary thermodynamics to be depolymerizable but lack the mechanical and thermal robustness needed for commercial adoption. This challenge provides an ideal opportunity for AI-driven design to develop such sustainable materials where multiple machine learning models for relevant polymer properties can work in tandem to optimize across various necessary objectives for creating industry relevant and sustainable polymers. One crucial property in determining the depolymerizability tendencies of polymers is the change in enthalpy (ΔH) of polymerization. To handle this property, a machine learning (ML) algorithm to predict DH, that utilizes both experimental and ab initio data for enhanced accuracy, has successfully been developed, and continues to be improved so that polymers can efficiently be screened for the potential to be depolymerizable. In addition, current mechanical and thermal ML polymer property predictors will also be retrained and improved to better account for the ROP chemical space. Moving forward, this work identifies robust screening criteria to identify recyclable polymers with the potential to replace conventional food packaging plastics such as polyethylene terephthalate (PET), high-density polyethylene (HDPE), low-density polyethylene (LDPE), and polypropylene (PP). These criteria will then be put to action using two generative algorithms, virtual forward synthesis and a genetic algorithm to screen through millions of hypothetical ROP polymers. Virtual forward synthesis screens commercially available monomers to discover promising ROP polymers that can be synthesized today, while the genetic algorithm looks to the future to discover new potential polymers, pushing the boundaries of truly recyclable plastics. Close collaborations with experimentalists to create the most promising polymers from this work are currently in place and will be strengthened in the hope that the work proposed can lead to tangible progress in the creation of sustainable plastics for a circular economy.