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DMREF: Collaborative Research: Computationally-driven Design of Advanced Block Polymer Nanomaterials

DMREF：协作研究：先进嵌段聚合物纳米材料的计算驱动设计

基本信息

批准号：
1725272
负责人：
Kevin Dorfman
金额：
$ 70万
依托单位：
University of Minnesota-Twin Cities
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-10-01 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1725272&HistoricalAwards=false
关键词：
DMREF Collaborative Research Computationally driven

项目摘要

Non-technical Description: Block polymers are macromolecules that contain segments or 'blocks' of repeated polymerized monomers of at least two types. Much as proteins have tremendous variation in property and function in biological systems by virtue of the choice and placement of amino acid residues along the polymer backbone, the properties of block polymers can be widely tuned by varying the length, placement, and chemical identity of their constituent blocks. Block polymers are the basis for many important types of soft materials such as elastomers and adhesives, but are increasingly important in applications such as advanced membranes for batteries and fuel cells, medical devices, and soft templates for patterning microelectronic devices. A current challenge in deploying block polymers in such applications is that the chemical design space is vast and there is very limited data and predictive ability connecting the chemical structure to the derivative properties in a given material. This project aims to dramatically accelerate block polymer materials discovery by closely coupling modern theory and simulation approaches with state-of-the-art synthesis and characterization. Through extensive experimental feedback to validate and continuously improve models and simulation methods, the project will build the foundations for a future in which in silico design of block polymers is routine.Technical Description: Block polymers are attractive for creating advanced materials with novel functionality by embedding multiple physical or chemical properties within a single compound. Such polymers are also attractive for manufacturing as their synthesis is scalable and they embed nanostructures spontaneously by thermodynamic driving forces arising from the incompatibility of the different blocks. However, as the demand for distinct desirable properties exhibited by a single material increases, so must the number of blocks. The corresponding design space increases geometrically with the number of blocks and block chemistries, making an intuition-based, trial-and-error approach infeasible. Instead, the project adopts a computationally-driven materials discovery approach, building on recent game-changing advances in self-consistent field theory and global optimization strategies for materials design and discovery. These computational strategies are coupled to an ambitious, advanced synthesis and characterization program capable of realizing the desired materials in practice. Through experimental feedback to validate and continuously improve models and simulation methods, the project will build the foundations for a future in which in silico design of block polymers is routine.

非技术描述：嵌段聚合物是含有至少两种类型的重复聚合单体的链段或“嵌段”的大分子。由于氨基酸残基在聚合物主链上的选择和位置，蛋白质在生物系统中的性质和功能有很大的变化，嵌段聚合物的性质可以通过改变其组成嵌段的长度、位置和化学特性来广泛调节。嵌段聚合物是许多重要类型的软材料如弹性体和粘合剂的基础，但在诸如用于电池和燃料电池的高级膜、医疗装置和用于图案化微电子装置的软模板的应用中越来越重要。在此类应用中部署嵌段聚合物的当前挑战是化学设计空间巨大，并且将给定材料中的化学结构与衍生物性质联系起来的数据和预测能力非常有限。该项目旨在通过将现代理论和模拟方法与最先进的合成和表征紧密结合，显着加速嵌段聚合物材料的发现。通过广泛的实验反馈来验证和不断改进模型和模拟方法，该项目将为嵌段聚合物的计算机设计成为常规奠定基础。技术描述：嵌段聚合物通过在单一化合物中嵌入多种物理或化学性质，具有创造具有新功能的先进材料的吸引力。这种聚合物对于制造也是有吸引力的，因为它们的合成是可扩展的，并且它们通过由不同嵌段的不相容性引起的热力学驱动力自发地嵌入纳米结构。然而，随着对由单一材料表现出的独特的期望性质的需求增加，嵌段的数量也必须增加。相应的设计空间随着嵌段和嵌段化学的数量呈几何级数增加，使得基于直觉的试错方法不可行。相反，该项目采用了计算驱动的材料发现方法，建立在自洽场理论和材料设计和发现的全局优化策略的最新进展基础上。这些计算策略被耦合到一个雄心勃勃的，先进的合成和表征程序，能够在实践中实现所需的材料。通过实验反馈来验证和不断改进模型和模拟方法，该项目将为嵌段聚合物的计算机设计成为常规的未来奠定基础。