EAGER: Multiscale Modeling of Mechanically-Interlocked Macromolecules

非技术性总结EAGER提案的这一奖项支持理论和计算研究和教育，旨在促进对环状分子物理性质的基本理解，这些环状分子相互连接形成一个称为卡塔纳聚合物的分子链。该项目的重点是研究聚合物索烃的结构和动力学。机械互锁大分子（MIM），如DNA和蛋白质链烷是大分子组装体，可以认为是通过一种由互锁环形成的“机械键”而不是通常的直接化学键保持在一起。这种新的结构预计将表现出独特的性能，特别是与线性链状分子，经典的聚合物相比。合成方法的局限性推迟了对MIM的研究。自20世纪50年代以来，人们一直在寻求改进的合成方法以显著提高MIM的产率。合成方法的最新进展，特别是“模板导向”合成，大大提高了MIM的产率，并获得了2016年诺贝尔化学奖。在这个项目中，PI将使用计算机模拟与机器学习（ML）相结合的方法来探索链状聚合物，并研究它们在不同物理环境中的结构和动力学。PI旨在扩展互锁大分子固有的基础物理学知识，这对于探索链状大分子在工业应用中的未开发潜力至关重要。机器学习方法将用于克服模拟障碍，从而能够预测索烃聚合物的新设计原理。在该项目中，PI还旨在预测未来合成，构建和表征的新型目标材料。该项目将为高中生和博士后研究人员提供教育经验。来自当地圣克莱门特-圣玛丽学校的高中生将参加这项拟议中的工作。本科生将通过高分子科学与高分子工程学院的NSF- REU中心参加。该奖项基于EAGER的提案，旨在支持理论和计算研究以及旨在促进对链型聚合物物理性质的基本理解的教育。机械互锁大分子（MIM）如索烃是通过拓扑约束而不是化学键保持在一起的大分子组装体，具有明确定义的拓扑相互作用，并且预期表现出与其线性对应物大不相同的各种独特性质。合成方法的局限性导致这一领域进展缓慢，直到最近开发出新的合成方法，“模板导向”合成，这大大提高了MIM的产率。该项目涉及使用全原子和粗粒度分子动力学模拟与机器学习（ML）相结合，以研究索烃的结构和动力学，包括表面和界面。机器学习方法将用于克服模拟的局限性，并能够预测索烃聚合物的新设计原则。 通过理论和模拟研究，PI旨在扩展互锁大分子内在的物理知识，这对于探索链状大分子的潜在工业应用至关重要。这项研究将为未来复杂聚合物体系的介观和多尺度建模提供基础。在该项目中，PI还旨在预测未来合成，构建和表征的新型目标材料。该研究将为高中到研究生阶段的学生提供教育机会。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.