Collaborative Research: CDI-Type I: Meta-Codes for Computational Kinetics

合作研究：CDI-Type I：计算动力学元代码

• 批准号: 1027765
• 负责人: Michael Falk
• 金额: $ 28万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1027765&HistoricalAwards=false
• 关键词: Collaborative; Research; CDI; Type; Meta

This award is part of the Cyber-enabled Discovery and Innovation CDI-Type I and the recipients are Michael Falk of Johns Hopkins University and Krishnakumar Garikipati of University of Michigan Ann Arbor.Materials are increasingly being applied in devices that derive their function from small-scale, and even nanometer-scale, features. Examples include advanced battery materials and quantum wires proposed for use in next generation opto-electronics. Features on these scales are subject to profound changes over time due to the thermally induced motion of atoms. The focus of this project is to develop the computational tools that are required for predicting the way these features evolve with time by developing computer programs referred to as "meta-codes." These meta-codes allow researchers to consider these problems by integrating knowledge obtained from the smallest scales, on which electrons control atomic interactions, to the largest scales, on which elastic interactions drive features to form or dissolve with time. The expected result is an automated, top-down working paradigm that naturally integrates tools from quantum mechanics, statistical physics and continuum elasticity. The intellectual impact comes both from an increased understanding of how to develop meta- codes to undertake multi-scale modeling and from a deeper understanding of the energy storage and electronic materials studied with these novel tools. The broader impact of this work arise from the development of an approach that integrates chemistry, physics and mechanics, and that can be used to refine our understanding of how a wide variety of materials systems evolve when there exist large spatial variations in composition and stress. The project includes active participation of students at the undergraduate, graduate and postdoctoral level, and incorporates a series of outreach activities that leverage those ongoing in the involved institutions.

该奖项是网络驱动的发现和创新 CDI-I 型的一部分，获奖者是约翰·霍普金斯大学的 Michael Falk 和密歇根大学安娜堡分校的 Krishnakumar Garikipati。材料越来越多地应用于从小尺度甚至纳米尺度特征中获得功能的设备。例子包括提议用于下一代光电器件的先进电池材料和量子线。 由于原子的热感应运动，这些尺度上的特征会随着时间的推移而发生深刻的变化。 该项目的重点是通过开发称为“元代码”的计算机程序来开发预测这些特征随时间演变的方式所需的计算工具。 这些元代码使研究人员能够通过整合从最小尺度（电子控制原子相互作用）到最大尺度（弹性相互作用驱动特征随时间形成或溶解）获得的知识来考虑这些问题。预期的结果是一种自动化、自上而下的工作范式，自然地集成了量子力学、统计物理学和连续弹性的工具。 智力影响既来自于对如何开发元代码以进行多尺度建模的更多理解，也来自对使用这些新颖工具研究的能量存储和电子材料的更深入理解。这项工作的更广泛影响源于一种集成化学、物理和力学的方法的开发，该方法可用于加深我们对当成分和应力存在较大空间变化时各种材料系统如何演变的理解。该项目包括本科生、研究生和博士后学生的积极参与，并结合了一系列利用相关机构正在进行的活动的外展活动。