CAREER: Cyber-enabled Multiscale Methodology for Hybrid Soft Materials-based Nanoparticle Design

数学与物理科学局的材料研究部和计算机与信息科学与工程局的高级网络基础设施办公室为该项目提供资金。该职业奖支持计算研究、网络基础设施开发和教育，以设计具有所需性能的纳米颗粒。纳米粒子无处不在，比如食品、药品、汽车和化妆品。纳米粒子的性质是由它们所包含的分子决定的，并且可以通过使用不同种类的分子来精确调节。制造具有特定性质和分子成分的纳米粒子需要了解分子之间如何相互作用并聚集在一起。鉴于可用分子的数量巨大，需要一种有效的方法来将纳米粒子的特性与其分子成分的特性联系起来。特别令人感兴趣的是由柔软材料制成的纳米颗粒，比如汽车轮胎、果冻和洗涤剂中的纳米颗粒，它们可以储存各种分子。这个CAREER项目支持基于混合软材料的纳米颗粒的计算设计的计算研究和教育。PI的方法将使用一种包括不同长度和时间尺度的基本物理和化学的方法。该方法将得到先进网络基础设施的发展和使用的帮助。这项研究将支持激发高中生对科学、工程和数学兴趣的活动。此外，这项研究将用于吸引和告知公众计算在社会材料设计中的作用和大学水平教育的好处。最后，通过提高本科生、研究生和科学界对材料设计的先进计算方法和工具的认识，本研究将增强和保持具有竞争力的科学和工程劳动力。技术摘要：数学和物理科学理事会的材料研究部以及计算机和信息科学与工程理事会的高级网络基础设施办公室为该项目提供资金。该职业奖支持计算研究、网络基础设施开发和教育，以设计具有所需性能的纳米颗粒。该项目旨在通过开发和使用网络支持的多尺度方法，提高设计具有特定结构-性能关系的混合软材料纳米颗粒（NPs）的能力。这种设计的概念将需要对分子构象、组织和流动性在不同分子物种的集体行为中的作用有一个基本的理解，从而对材料特性有一个基本的理解。这项研究是在软材料和先进计算的界面，并提供了一个机会，以提高意识，兴趣，招聘，保留和培训有竞争力的劳动力在科学，技术，工程和数学领域。PI试图设计具有优化的形态以存储各种分子的立体稳定的杂交NPs。NP设计将需要理解分子特征和混合软材料所需性能之间的联系。这将通过开发和使用多尺度方法来促进，该方法可以通过将分子动力学模拟和分析工具与先进的网络基础设施相结合，将NP的组成细节与其所需属性联系起来。该计划将通过三个目标来实现：(1)开发混合NP设计；(2)阐明pH对杂化NP设计的作用；(3)预测和验证包含替代化学物质的杂化NP设计。通过理解分子特性和结构-性能关系之间的关系，将大大加速预测具有理想性能的软材料基纳米颗粒。设计规则和方法都可以推广到其他具有目标结构-性能关系的多组分软材料系统。此外，在虚拟软材料设计中使用先进的计算工具可以使社区采用新的计算方法。这将促进新的基于软材料的创新和技术的加速发展。

NONTECHNICAL SUMMARYThe Division of Materials Research in the Mathematical and Physical Sciences Directorate and the Office of Advanced Cyberinfrastructure in the Directorate for Computer and Information Science and Engineering contribute funds for this project. This CAREER award supports computational research, cyberinfrastructure development, and education toward the ability to design nanoparticles with desired properties. Nanoparticles are encountered everywhere such as food, drugs, cars and cosmetics. The properties of nanoparticles are determined by the molecules they encompass, and can be precisely adjusted by using different kinds of molecules. Creating nanoparticles with specific properties and molecular constituents requires understanding how the molecules interact with each other and pack together. Given the vast number of molecules available, an efficient method is required to relate the characteristics of a nanoparticle to the properties of its molecular constituents. Of special interest are nanoparticles made of soft materials, such as those in car tires, jello and detergents, which can store various molecules. This CAREER project supports computational research and education on the computational design of mixed soft materials-based nanoparticles with desired characteristics. The PI's approach will use a method that includes essential physics and chemistry at different scales of length and time. The method will be aided by the development and use of advanced cyberinfrastructure. This research will support activities to stimulate the interest of high school students in science, engineering and mathematics. In addition, the research will be used to engage and inform the general public of the role of computation in materials design to society and benefits of University-level education. Finally, the research will enhance and maintain a competitive science and engineering workforce by increasing awareness of advanced computing methods and tools for materials design among undergraduate, graduate students and the scientific community.TECHNICAL SUMMARYThe Division of Materials Research in the Mathematical and Physical Sciences Directorate and the Office of Advanced Cyberinfrastructure in the Directorate for Computer and Information Science and Engineering contribute funds for this project. This CAREER award supports computational research, cyberinfrastructure development, and education toward the ability to design nanoparticles with desired properties. The PI aims to advance the ability to design of hybrid soft materials-based nanoparticles (NPs) with specific structure-property relations aided by the development and use of a cyber-enabled multiscale methodology. The conception of such designs will require a fundamental understanding of the role of molecular conformation, organization and mobility on the collective behavior of the distinct molecular species, and thereby, on material properties. The research lies at the interface of soft materials and advanced computing, and affords an opportunity to increase awareness, interest, recruitment, retention and training of a competitive workforce in science, technology, engineering, and mathematics areas.The PI seeks to design sterically stable hybrid NPs with morphologies optimized to store various molecules. The NP designs will require understanding the links between molecular traits and desired properties of hybrid soft materials. This will be facilitated by the development and use of a multiscale method that can link the compositional details of the NP to its desired attributes by integrating Molecular Dynamics simulations and analysis tools with advanced cyberinfrastructure. This plan will be realized through three objectives: (1) Development of hybrid NP designs; (2) Elucidation of the role of pH on hybrid NP designs, and (3) Prediction and validation of hybrid NP designs encompassing alternate chemical species.The prediction of soft materials-based nanoparticles with desired properties will be significantly accelerated by understanding the relationship between molecular traits and structure-property relations. Both the design rules and the method can be extended to conceive other multicomponent soft material-based systems with targeted structure-property relations. In addition, the use of advanced computing tools in virtual soft materials design can nucleate the adoption of new computational methodologies by the community. This will facilitate the accelerated development of new soft materials-based innovations and technologies.