DMREF: Collaborative: Computationally Driven Discovery and Engineering of Multiblock Polymer Nanostructures Using Genetic Algorithms

DMREF：协作：使用遗传算法计算驱动的多嵌段聚合物纳米结构的发现和工程

• 批准号: 1332842
• 负责人: Glenn Fredrickson
• 金额: $ 39万
• 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: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1332842&HistoricalAwards=false
• 关键词: DMREF; Collaborative; Computationally; Driven; Discovery

****Technical Abstract****This computationally driven discovery program aims to disrupt the status quo for the design of multiblock materials. The research centers on the marriage of pseudo-spectral self-consistent field theory (SCFT) and real-space genetic algorithms (GAs), with a tight coupling to experimental synthesis and characterization. While widely used in polymer science and bioinformatics, respectively, SCFT and GAs have not been previously integrated to tackle problems of block polymer discovery and design. The approach adopted here addresses the challenges of large parameter spaces and polymorphism. It also solves the "inverse problem" of identifying multiblock sequences and compositions that can produce a desired nanoscale morphology, without resorting to exhaustive, unguided searches through parameter space. The computational effort is synergistically and iteratively combined with an experimental program that includes state-of-the-art synthesis, processing, and characterization tools. The experimental work will validate the computational methodology, including parameterization of the models, and provide inspiration for attractive and synthetically accessible design targets. This combined approach will dramatically reduce the timescale for discovery, design, and deployment of new multiblock polymers as advanced functional materials.****Non-Technical Abstract****This collaborative effort between researchers at the University of California, Santa Barbara and the University of Minnesota will develop discovery tools that will enable the rational, computationally-assisted design of multiblock polymers for applications in medicine, microelectronics, separations, and energy production and storage, among others. Complicating factors in this class of soft materials are the myriad parameters that dictate molecular architecture, block sequence, and interactions and the wide range of self-assembled nanostructures that are possible. Through a concerted and iterative combination of theory, simulation, and experiment, global optimization tools will be devised and validated to predict the forward and reverse relationship between polymer architecture and nanostructure. The discovery tools developed in this program will be made widely available to the industrial and academic polymer materials community through a web-based job submission program hosted at the Minnesota Supercomputer Institute, and a searchable database will be constructed from the structure/sequence/morphology maps that result over the course of the project. Outreach to industry will be accomplished by leveraging the established and highly successful industrial consortiums at UCSB (Complex Fluids Design Consortium) and UMN (IPrime). Personnel on the project will be trained in and enhance the rich multidisciplinary research environments afforded by the existing MRSECs at UMN and UCSB.This award is funded by the Division of Materials Research (DMR) and the Division of Mathematical Sciences (DMS).

* 技术摘要 * 这个计算驱动的发现计划旨在打破多块材料设计的现状。研究中心的婚姻的伪谱自洽场理论（SCFT）和实空间遗传算法（GAs），与实验合成和表征的紧密耦合。虽然分别广泛用于聚合物科学和生物信息学，但SCFT和GAs先前尚未集成以解决嵌段聚合物发现和设计的问题。这里采用的方法解决了大参数空间和多态性的挑战。它还解决了识别多嵌段序列和组合物的“逆问题”，这些序列和组合物可以产生所需的纳米级形态，而无需通过参数空间进行穷举的无指导搜索。计算工作是协同和迭代结合的实验程序，包括国家的最先进的合成，处理和表征工具。实验工作将验证计算方法，包括参数化的模型，并提供有吸引力的和综合访问的设计目标的灵感。这种组合方法将大大缩短发现、设计和部署新型多嵌段聚合物作为先进功能材料的时间。*非技术摘要 * 加州大学、圣巴巴拉和明尼苏达大学的研究人员将共同努力开发发现工具，使多嵌段聚合物的合理计算辅助设计能够应用于医学、微电子、分离、能源生产和储存等领域。这类软材料中的复杂因素是决定分子结构、嵌段序列和相互作用的无数参数，以及可能的自组装纳米结构的广泛范围。通过理论，模拟和实验的协调和迭代组合，全局优化工具将被设计和验证，以预测聚合物结构和纳米结构之间的正向和反向关系。在该计划中开发的发现工具将通过明尼苏达州超级计算机研究所托管的基于网络的作业提交程序广泛提供给工业和学术聚合物材料社区，并且将从该项目过程中产生的结构/序列/形态图构建可搜索的数据库。将通过利用UCSB（复杂流体设计联盟）和UMN（IPrime）的成熟和非常成功的工业联盟来实现对工业的推广。该项目的人员将接受培训，并加强由UMN和UCSB现有的MRSECs提供的丰富的多学科研究环境。该奖项由材料研究部（DMR）和数学科学部（DMS）资助。