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; 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****此计算驱动的发现程序旨在破坏设计多块材料的现状。研究以伪柔性自洽场理论（SCFT）和真实空间遗传算法（GAS）的结合为中心，与实验合成和表征紧密耦合。虽然广泛用于聚合物科学和生物信息学，但以前尚未集成SCFT和气体来解决块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。它还解决了识别可以产生所需纳米级形态的多块序列和组成的“反问题”，而无需通过参数空间诉诸详尽的，无指导的搜索。计算工作在协同和迭代上与包括最新合成，处理和表征工具的实验程序相结合。实验工作将验证计算方法，包括模型的参数化，并为有吸引力和合成的设计目标提供灵感。这种组合的方法将大大减少新的多块聚合物的发现，设计和部署的时间尺度。微电子，分离以及能源生产和存储等。这类软材料中的复杂因素是指决定分子结构，块序列和相互作用以及可能的各种自我组装的纳米结构的无数参数。通过理论，仿真和实验的一致和迭代组合，将设计和验证全局优化工具，以预测聚合物体系结构与纳米结构之间的正向和反向关系。该计划中开发的发现工具将通过在明尼苏达州超级计算机研究所托管的基于Web的工作提交计划为工业和学术聚合物材料社区广泛使用，并将根据在项目过程中导致的结构/序列/序列/形态图构建可搜索的数据库。通过利用UCSB（复杂的流体设计财团）和UMN（IPRIME）的建立且非常成功的工业联盟来实现对工业的宣传。该项目的人员将接受培训，并增强UMN和UCSB现有MRSEC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（DMS）（DMS）资助。