AF:Small:Coarse-Grained Algorithms for Soft Matter

AF:Small:软物质的粗粒度算法

• 批准号: 0915718
• 负责人: Narayana Aluru
• 金额: $ 45万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0915718&HistoricalAwards=false
• 关键词: AF; Small; Coarse; Grained; Algorithms

Proposal Number: 0915718Title: AF:Small:Coarse-Grained Algorithms for Soft MatterPrincipal Investigator: N. R. Aluru Institution: University of Illinois at Urbana-ChampaignAbstractSoft matter (e.g. liquids, polymers, biopolymers, etc.) plays an important role in many emerging technologies in engineering and science. The physics of soft matter at macroscopic scales has been investigated for many decades. There is now a good understanding of how to manipulate soft matter at macroscopic scales. The physics of soft matter in confined environments (referring to the behavior of soft matter in constrained spaces) can be quite different from its macroscopic counterpart and many fundamental issues still remain. Soft matter in confined environments can find applications in important technological areas such as energy, health, sensing, sequencing, separation, etc. As a result, soft matter in confined environments has now gained significant interest from the scientific community. Various computational techniques can be used to understand physical, chemical and biological properties of soft matter. However, many of the existing techniques are either too expensive or not accurate enough to perform detailed studies. The objective of this research is to develop advanced computational algorithms to enable a detailed understanding of soft matter in confined environments. Even though quantum-mechanical and atomistic molecular dynamics simulations can be used to understand soft matter in confined spaces, they are limited to small length and short time scales. Mesoscopic methods, such as Brownian dynamics, Monte Carlo, lattice Boltzmann, dissipative particle dynamics, etc., can be used to overcome the limitations of quantum and atomistic molecular dynamics simulations, but, structural accuracy is a key issue in these methods. The objective of this research is to develop novel coarse-grained algorithms where inter-atomic potentials, widely used in atomistic simulation of soft matter, are directly incorporated into advanced physical theories. The inter-atomic potentials will be coarse-grained to ensure structural consistency. The inter-atomic potential based coarse-grained algorithms will be applied for several challenging examples of soft matter. The accuracy of the structural prediction from coarse-grained algorithms will be compared with that from atomistic simulations. It is anticipated that inter-atomic potential based coarse-grained algorithms will be many orders of magnitude faster than purely atomistic simulations and the development of such algorithms will not only elucidate the fundamental aspects of soft matter in confined spaces, but will also lead to rapid computational prototyping of various applications of soft matter.The proposed research is at the cross-roads of several engineering and science disciplines. As a result, the development of inter-atomic potential based coarse-grained algorithms for soft matter will impact several disciplines and application areas. Some of the application areas that could benefit from this fundamental research are energy, sensing, health, sequencing, separation, etc. The main efforts of this project will result in the education of students and postdoctoral associates in the highly interdisciplinary area of soft matter. The research results from this project will be broadly disseminated via journal and conference publications, presentations at meetings and workshops, software, courses taught by the PI in the Department of Mechanical Science and Engineering and summer schools offered at University of Illinois.

提案编号：0915718标题：AF：小：软物质的粗粒度算法主要研究者：N。R. Aluru机构：伊利诺伊大学厄巴纳-香槟分校摘要软物质（如液体，聚合物，生物聚合物等）在工程和科学的许多新兴技术中发挥着重要作用。在宏观尺度下的软物质物理学已经研究了几十年。现在人们对如何在宏观尺度上操纵软物质有了很好的理解。受限环境中的软物质物理学（指软物质在受限空间中的行为）与宏观环境中的软物质物理学有很大的不同，许多基本问题仍然存在。受限环境中的软物质可以在重要的技术领域中找到应用，例如能源，健康，传感，测序，分离等，因此，受限环境中的软物质现在已经引起了科学界的极大兴趣。各种计算技术可以用来理解软物质的物理，化学和生物学特性。然而，许多现有的技术要么太昂贵，要么不够准确，无法进行详细的研究。这项研究的目的是开发先进的计算算法，使在密闭环境中的软物质的详细了解。尽管量子力学和原子分子动力学模拟可以用来理解封闭空间中的软物质，但它们仅限于小长度和短时间尺度。介观方法，如布朗动力学、蒙特卡罗、格子玻尔兹曼、耗散粒子动力学等，可以用来克服量子和原子分子动力学模拟的局限性，但是，结构的准确性是这些方法中的一个关键问题。本研究的目的是开发新的粗粒度算法，原子间的潜力，广泛用于原子模拟软物质，直接纳入先进的物理理论。原子间势将被粗粒度化以确保结构一致性。基于原子间势的粗粒度算法将应用于软物质的几个具有挑战性的例子。粗粒度算法的结构预测的准确性将与原子模拟进行比较。预计基于原子间势的粗粒度算法将比纯原子模拟快许多数量级，这种算法的发展不仅将阐明有限空间中软物质的基本方面，而且还将导致软物质各种应用的快速计算原型。因此，基于原子间势的软物质粗粒度算法的发展将影响多个学科和应用领域。一些应用领域，可以受益于这一基础研究是能源，传感，健康，测序，分离等，该项目的主要努力将导致在软物质的高度跨学科领域的学生和博士后的教育。该项目的研究成果将通过期刊和会议出版物，会议和研讨会上的演讲，软件，PI在机械科学与工程系教授的课程以及伊利诺伊大学提供的暑期学校广泛传播。