Aubrey R. Toland
Advisor: Rampi Ramprasad
will defend a doctoral thesis entitled,
AI-DRIVEN DESIGN OF CHEMICALLY RECYCLABLE POLYMERS TO
REPLACE COMMODITY PLASTICS
On
Friday, November 21st at 12 p.m.
Van Leer Room C241
Virtually via MS Teams or Zoom
Committee
Dr. Rampi Ramprasad- School of Materials Science and Engineering (advisor)
Dr. Will Gutekunst - School of Chemistry and Biochemistry
Dr. Aaron Stebner - School of Materials Science and Engineering
Dr. Seung Soon Jang - School of Materials Science and Engineering
Dr. Keith Hearon - 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. Herein multiple machine learning (ML) models for relevant polymer properties work in tandem with generative algorithms 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 (∆H) of polymerization. To handle this property, a ML algorithm to predict ∆H, 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 have also been 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 plastics such as polyethylene
terephthalate (PET), high-density polyethylene (HDPE), polystyrene (PS), and polypropylene (PP). These criteria are then be put to action using two generative algorithms, Virtual Forward Synthesis (VFS) and a Genetic Algorithm (GA) to screen through millions of hypothetical polymers synthesized via ring opening polymerization (ROP polymers). VFS
screens commercially available monomers to discover promising ROP polymers that can be synthesized today, while the GA 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 have been in place and it is the true goal that recommended polymers from this work result in tangible progress in the
creation of sustainable plastics for a circular economy.