EAGER: Multiscale Modeling of Mechanically-Interlocked Macromolecules

非技术总结这个奖项是根据一项迫切的提议颁发的，旨在促进对环状分子物理性质的基本了解，这些研究和教育旨在促进对环状分子物理性质的基本了解，这些环状分子相互作用，形成一种称为卡塔烷聚合物的分子链。该项目专注于研究聚合物链环的结构和动力学。机械互锁大分子(MIM)，如DNA和蛋白质链烷，是可以被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连接在一起的大分子组件。这种新颖的结构有望显示出独特的性质，特别是与传统的线型链状分子相比。合成方法的局限性推迟了对MIMs的研究。自20世纪50年代以来，S一直在寻求改进的合成方法，以大幅提高MIMs的产率。合成方法，特别是“模板导向”合成方面的最新进展，大大提高了MIMs的产率，并使其获得了2016年诺贝尔化学奖。在这个项目中，PI将使用计算机模拟和机器学习(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的目标是扩展互锁大分子内在的潜在物理知识，这对于探索工业应用中链接大分子的未开发潜力至关重要。机器学习方法将被用来克服模拟障碍，使能够预测新的环戊二烯聚合物的设计原理。在这个项目中，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。来自当地圣文森特-圣彼得堡的高中生。玛丽学校将参与这项拟议的工作。本科生将通过聚合物科学和聚合物工程学院的NSF-REU中心参与。技术总结这个奖项是在一份热切的提案上颁发的，旨在支持旨在促进对链状聚合物物理性质的基本了解的理论和计算研究和教育。机械互锁大分子(MIM)，如链烷，是通过拓扑约束而不是化学键结合在一起的大分子组件，具有明确的拓扑相互作用，并有望显示出与其线性对应的许多不同的独特性质。合成方法的局限性导致这一领域的进展缓慢，直到最近开发出新的合成方法--模板导向合成，大大提高了MIMs的产率。这个项目涉及使用全原子和粗粒度分子动力学模拟并结合机器学习(ML)来研究链烷的结构和动力学，包括在表面和界面上。机器学习方法将被用来克服模拟的局限性，并使能够预测新的环戊二烯聚合物的设计原理。通过基于理论和模拟的研究，PI旨在扩展互锁大分子内在的潜在物理知识，这对于探索链状大分子潜在的工业应用至关重要。这一研究将为未来复杂聚合物体系的中尺度和多尺度建模提供基础。在这个项目中，PI还旨在预测未来合成、构建和表征的新型目标材料。这项研究将为从高中到研究生水平的学生提供教育机会。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.