EAGER: Multiscale Modeling of Mechanically-Interlocked Macromolecules

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NONTECHNICAL SUMMARYThis award made on an EAGER proposal supports theoretical and computational research and education aimed at advancing fundamental understanding of the physical properties of ring-like molecules interlocked to form a molecular chain called a catanane polymer. The project is focused on investigating the structure and dynamics of polymer catenanes. Mechanically-interlocked macromolecules (MIMs) such as DNA and protein catenanes are macromolecular assemblies that can be thought of as being held together by a kind of "mechanical bond" formed from interlocking rings rather than usual direct chemical bonds. This novel structure is expected to exhibit unique properties, particularly in comparison with those of linear chain-like molecules, classic polymers. Limitations in synthesis approaches have delayed research on MIMs. Since the 1950's improved synthesis methods were sought to substantially increase the yield of MIMs. Recent advances in synthetic methods, particularly "template-directed" synthesis, have substantially improved yields for MIMs and led to the 2016 Nobel prize in Chemistry. This opens research into understanding the unique physical properties of MIMs, as well as their applications.In this project, the PI will use computer simulation in tandem with machine learning (ML) to explore catenated polymers and investigate their structure and dynamics in different physical environments. The PI aims to expand knowledge of the underlying physics inherent in interlocked macromolecules that is critical in exploring the untapped potential of catenated macromolecules for industrial applications. Machine learning approaches will be used to overcome simulation barriers enabling the prediction of new design principles for catenane polymers. In this project, the PI also aims to predict novel target materials for future synthesis, construction and characterization. This project will provide educational experiences for high school students to postdoctoral researchers. High school students from the local St. Vincent-St. Mary school will participate in the proposed work. Undergraduate students will participate through the NSF- REU center at the College of Polymer Science and Polymer Engineering. TECHNICAL SUMMARYThis award made on an EAGER proposal supports theoretical and computational research and education aimed at advancing fundamental understanding of the physical properties of catenated polymers. Mechanically-interlocked macromolecules (MIMs) such as catenanes are macromolecular assemblies held together by topological constraints rather than chemical bonds, possess well-defined topological interactions, and are expected to exhibit a variety of unique properties that are much different than their linear counterparts. Limitations in synthesis approaches has led to slow progress in this area, until the recent development of new synthetic methods, "template-directed" synthesis, which substantially improved yields for MIMs. This project involves the use of all-atom and coarse-grained molecular dynamics simulations in tandem with machine learning (ML) to investigate the structure and dynamics of catenanes, including at surfaces and interfaces. Machine learning approaches will be used to overcome limitations of simulations and enable the prediction of new design principles for catenane polymers. Through theoretical and simulation-based research, the PI aims to expand knowledge of the underlying physics inherent in interlocked macromolecules that is critical in exploring potential industrial applications of catenated macromolecules.This research will provide groundwork for future mesoscale and multiscale modeling of complex polymeric systems. In this project, the PI also aims to predict novel target materials for future synthesis, construction and characterization. The research will provide educational opportunities for students from high school to graduate level.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.