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：
1725414
负责人：
Glenn Fredrickson
金额：
$ 50万
依托单位：
University of California-Santa Barbara
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 2021-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1725414&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：块聚合物是大分子，其中包含至少两种类型的重复聚合单体的片段或“块”。蛋白质的选择和放置在聚合物主链沿聚合物骨架上的选择和放置，蛋白质在生物系统中具有巨大变化，可以通过改变其组成块的长度，放置和化学身份来广泛调整块聚合物的性能。块聚合物是许多重要类型的软材料（例如弹性体和胶粘剂）的基础，但在应用程序中越来越重要，例如用于电池和燃料电池的高级膜，医疗设备以及用于图案微电动设备的软模板。在这种应用中部署块聚合物的当前挑战是，化学设计空间很大，并且将化学结构与给定材料中的衍生性质连接到衍生物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耦合，从而极大地加速了块聚合物材料的发现。通过广泛的实验反馈以验证和不断改进模型和仿真方法，该项目将为未来建立基础，在该未来中，在块聚合物的硅设计中是常规的。技术描述：块聚合物通过将多个物理或化学性质嵌入单个化合物中，可用于创建具有新功能的高级材料。由于它们的合成是可扩展的，因此这些聚合物也对制造具有吸引力，并且它们是由由于不同块的不兼容而自发地嵌入纳米结构的。但是，随着单个材料对独特所需特性的需求增加，块的数量也必须增加。相应的设计空间随着块和块化学的数量而几何地增加，这使得基于直觉的试验方法是不可行的。取而代之的是，该项目采用了一种计算驱动的材料发现方法，这是基于最新的自洽场理论的最新进展和材料设计和发现的全球优化策略的基础。这些计算策略与雄心勃勃的，先进的合成和表征计划结合在一起，能够实现实践中所需的材料。通过实验反馈以验证和不断改进模型和仿真方法，该项目将为未来的未来建立基础，在该未来中，块聚合物的硅设计是常规的。