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：0915718TITLE：AF：小：软化物质原理研究者的粗粒算法：N。R. Aluru Institation：Urbana-Champaignabstractsoft Matter（例如液体，聚合物，生物聚合物等）在许多Emerging技术中扮演重要角色。数十年来研究了宏观尺度的软物质物理学。现在，人们对如何在宏观尺度上操纵软物质有很好的了解。在受限环境中软物质的物理学（指约束空间中软物质的行为）可能与其宏观对应物完全不同，并且仍然存在许多基本问题。在狭窄的环境中，软物质可以在重要技术领域（例如能源，健康，传感，测序，分离等）中找到应用。因此，在受限环境中的软物质现已引起了科学界的重大兴趣。各种计算技术可用于了解软物质的物理，化学和生物学特性。但是，许多现有技术要么太贵了，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，以在受限环境中详细了解软物质。即使可以使用量子力学和原子分子动力学模拟来理解密闭空间中的软物质，但它们的长度且时间很小，时间尺度很短。可以使用介观方法，例如布朗动力学，蒙特卡洛，晶格鲍尔茨曼，耗散粒子动力学等，以克服量子和原子分子动力学模拟的局限性，但是结构性准确性是这些方法中的关键问题。这项研究的目的是开发新颖的粗粒算法，在这些算法中，在软物质原子模拟中广泛使用的原子间电位直接纳入了先进的物理理论中。原子间电位将是粗粒，以确保结构一致性。基于原子间潜在的粗粒算法将用于几个具有挑战性的软物质实例。将粗粒算法的结构预测的准确性与原子模拟相比。可以预见的是，基于原子的粗粒算法将比纯粹的原子模拟快很多级数量级，并且这种算法的发展不仅会阐明限制空间中软物质的基本方面，而且还将导致拟议的多个工程学的跨越多个工程和多个工程学的计算。结果，针对软物质的基于原子间潜在的粗粒算法的发展将影响几个学科和应用领域。这项基本研究可能受益的某些应用领域是能量，感应，健康，测序，分离等。该项目的主要工作将导致在高度跨学科的软件跨学科领域的学生和博士后同伴的教育。该项目的研究结果将通过期刊和会议出版物，会议和研讨会上的演讲，软件，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的介绍。