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

****技术摘要****这个计算驱动的发现计划旨在打破多嵌段材料设计的现状。该研究的重点是伪谱自洽场论（SCFT）和实空间遗传算法（GA）的结合，与实验综合和表征紧密耦合。虽然 SCFT 和 GA 分别广泛应用于聚合物科学和生物信息学，但此前尚未集成来解决嵌段聚合物的发现和设计问题。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”，这些多嵌段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详尽的、无引导的搜索。计算工作与包括最先进的合成、处理和表征工具的实验程序协同迭代地结合在一起。实验工作将验证计算方法，包括模型的参数化，并为有吸引力且综合可访问的设计目标提供灵感。这种组合方法将大大缩短作为先进功能材料的新型多嵌段聚合物的发现、设计和部署的时间。****非技术摘要****加州大学圣塔芭芭拉分校和明尼苏达大学的研究人员之间的合作将开发发现工具，使多嵌段聚合物能够在医学、微电子、分离、能源生产和存储等领域进行合理的计算辅助设计。这类软材料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各种自组装纳米结构。通过理论、模拟和实验的协调和迭代组合，将设计和验证全局优化工具，以预测聚合物结构和纳米结构之间的正向和反向关系。该项目开发的发现工具将通过明尼苏达超级计算机研究所主办的基于网络的作业提交项目广泛提供给工业和学术界的聚合物材料界，并且将根据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) 等成熟且非常成功的工业联盟来完成对工业的推广。该项目的人员将在 UMN 和 UCSB 现有的 MRSEC 提供的丰富的多学科研究环境中接受培训并加强该环境。该奖项由材料研究部 (DMR) 和数学科学部 (DMS) 资助。

****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).