FRG: Development and Validation of Novel Computational Tools for Modeling the Growth and Self-Assembly of Crystalline Nanostructures

FRG：用于模拟晶体纳米结构的生长和自组装的新型计算工具的开发和验证

• 批准号: 1105409
• 负责人: Katsuyo Thornton
• 金额: $ 90万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1105409&HistoricalAwards=false
• 关键词: FRG; Development; Validation; Novel; Computational

TECHNICAL SUMMARY:This award supports theoretical research and educational activities in computational materials science, with a focus on the controlled growth of nanostructured materials.Nanostructured materials, including semiconductor quantum dots and nanowires, hold the promise to yield revolutionary new technologies. The realization of this promise has been hindered to date by the challenges inherent in reproducibly synthesizing and assembling arrays of such nanostructures with controlled morphologies and compositions. Computational modeling is essential for understanding the complex fundamental processes underlying nanostructure growth and self-assembly. However, current state-of-the-art computational methods are not well suited to such studies because of the difficulty in accounting for the atomistic processes that control growth, while simulating over the larger length and time scales associated with self-assembly. The PIs, three materials scientists and a mathematician, propose to develop a new computational methodology that addresses these issues, in order to investigate quantum-dot formation in thin-film heteroepitaxy and nanowire growth mediated by liquid catalysts. This multidisciplinary project addresses the challenge of understanding multiscale phenomena associated with the formation of nanostructures by exploiting recent developments in phase field crystal (PFC) models, which resolve atomic spatial scales on diffusive time scales. The PFC method naturally incorporates elastic/plastic deformations and crystalline defects, and has already been used to simulate interfacial evolution during solid-liquid phase transitions. The team will develop a new PFC-based computational methodology for modeling solid-vapor, liquid-vapor and faceted solid-liquid interfaces, which are commonly present during the growth of crystalline nanostructures. The models will be parameterized and validated with the aid of atomistic simulations and experimental results. Amplitude/phase equations similar to traditional phase field models will be derived to allow larger domains to be simulated. This will reveal how atomistic features and processes affect morphological evolution at larger scales. We will develop new efficient numerical algorithms that will enable simulations of large 3D systems, as well as novel tools for data extraction and visualization. These tools will advance the field of computational materials science by providing a framework for the computational discovery of the fundamental mechanisms underlying synthesis and assembly of nanostructures.Courses on crystal growth for high school students are planned as part of the California State Summer School for Mathematics and Science at UC Irvine, along with additional outreach activities at the Ann Arbor Hands-On Museum. These activities will help develop the future generation of mathematicians, scientists and engineers. Graduate students will receive interdisciplinary training and will present their findings at conferences, enhancing their educational experiences. Furthermore, a symposium on the PFC approach will be organized. In collaboration with the National Institute of Standards and Technology, a FiPy version of the PFC codes will be developed and will be disseminated through their website for use in education and research.NON-TECHNICAL SUMMARY:This award supports theoretical research and educational activities in computational materials science, with a focus on the controlled growth of nanostructured materials.Artificial materials composed of building blocks on the scale of some ten thousand times smaller than the width of a human hair but still larger than an atom can have unique properties and capabilities that differ dramatically from bulk crystalline materials. The properties of these nanostructured materials and their lean tiny cousins, nanostructures, can be controlled through the way the building blocks are arranged; they may even arrange themselves through a process of self-assembly. Nanostructured materials including semiconductor quantum dots and nanowires, hold the promise to yield revolutionary new technologies. Realizing this promise has been hindered by difficulties in controlling their structures and compositions. The use of computers to model nanostructures and nanostructured materials is essential for understanding the complex fundamental processes underlying nanostructure growth and self-assembly. However, current state-of-the-art computational methods are not well suited to such studies because of the difficulty in accounting for the atomic scale processes that control growth, while simulating over the larger length and time scales associated with self-assembly, involving the organization of the building blocks. This grant will support the development of a new computational methodology that addresses these issues, in order to investigate quantum-dot formation and nanowire growth. The team will also develop new efficient numerical algorithms that will enable simulations of large three-dimensional systems, as well as novel tools for data extraction and visualization. These tools will advance the field of computational materials science by providing a framework for computational discovery of the fundamental mechanisms underlying synthesis and assembly of nanostructures.Courses on crystal growth for high school students are proposed as part of the California State Summer School for Mathematics and Science at UC Irvine, and additional outreach activities are planned at the Ann Arbor Hands-On Museum. These activities will help develop the future generation of mathematicians, scientists and engineers. Graduate students will receive interdisciplinary training and will present their findings at conferences, enhancing their educational experiences. Furthermore, a symposium on aforementioned computational techniques will be organized. In collaboration with the National Institute of Standards and Technology, a Python-script version of the simulation software will be developed and will be disseminated through their website for use in education and research.

技术概述：该奖项支持计算材料科学的理论研究和教育活动，重点是纳米结构材料的受控生长。纳米结构材料，包括半导体量子点和纳米线，有望产生革命性的新技术。迄今为止，这一前景的实现一直受到可再生合成和组装具有控制形态和成分的纳米结构阵列所固有的挑战的阻碍。计算建模对于理解纳米结构生长和自组装背后的复杂基本过程至关重要。然而，目前最先进的计算方法并不适合这样的研究，因为在计算控制生长的原子过程时存在困难，同时模拟与自组装相关的更大的长度和时间尺度。三位材料科学家和一位数学家提议开发一种新的计算方法来解决这些问题，以研究薄膜异质外延中的量子点形成和液体催化剂介导的纳米线生长。这个多学科项目通过利用相场晶体（PFC）模型的最新发展，解决了理解与纳米结构形成相关的多尺度现象的挑战，该模型在扩散时间尺度上解决了原子空间尺度。PFC方法自然地包含了弹性/塑性变形和晶体缺陷，并且已经用于模拟固液相变过程中的界面演变。该团队将开发一种新的基于pfc的计算方法，用于模拟固体-蒸汽、液体-蒸汽和多面固体-液体界面，这些界面通常存在于晶体纳米结构的生长过程中。这些模型将被参数化，并借助原子模拟和实验结果进行验证。振幅/相位方程类似于传统的相场模型将得到允许更大的域进行模拟。这将揭示原子特征和过程如何在更大的尺度上影响形态进化。我们将开发新的高效数值算法，以实现大型3D系统的模拟，以及用于数据提取和可视化的新工具。这些工具将通过为纳米结构合成和组装的基本机制的计算发现提供一个框架，从而推动计算材料科学领域的发展。针对高中生的晶体生长课程计划作为加州大学欧文分校的加州州立数学和科学暑期学校的一部分，以及安娜堡动手博物馆的额外推广活动。这些活动将有助于培养下一代数学家、科学家和工程师。研究生将接受跨学科的培训，并将在会议上展示他们的研究成果，以提高他们的教育经验。此外，还将组织一次关于PFC方法的专题讨论会。与国家标准与技术研究所合作，将开发一种PFC代码的FiPy版本，并将通过其网站传播，以供教育和研究使用。非技术总结：该奖项支持计算材料科学的理论研究和教育活动，重点是纳米结构材料的受控生长。人造材料是由比人类头发丝宽度小一万倍但仍比原子大的构件组成的，它具有与大块晶体材料截然不同的独特特性和能力。这些纳米结构材料和它们的微小表亲纳米结构的特性可以通过构建模块的排列方式来控制；它们甚至可以通过自我组装的过程来排列自己。包括半导体量子点和纳米线在内的纳米结构材料有望产生革命性的新技术。实现这一前景一直受到控制其结构和组成方面的困难的阻碍。利用计算机模拟纳米结构和纳米结构材料对于理解纳米结构生长和自组装背后的复杂基本过程至关重要。然而，目前最先进的计算方法并不适合于这样的研究，因为在计算控制生长的原子尺度过程时存在困难，而在与自组装相关的更大的长度和时间尺度上进行模拟，涉及构建块的组织。这笔拨款将支持开发一种新的计算方法来解决这些问题，以研究量子点的形成和纳米线的生长。该团队还将开发新的高效数值算法，用于模拟大型三维系统，以及用于数据提取和可视化的新工具。这些工具将通过为纳米结构合成和组装的基本机制的计算发现提供一个框架，从而推动计算材料科学领域的发展。针对高中生的晶体生长课程被提议作为加州大学欧文分校的加州州立数学和科学暑期学校的一部分，并计划在安娜堡动手博物馆开展额外的推广活动。这些活动将有助于培养下一代数学家、科学家和工程师。研究生将接受跨学科的培训，并将在会议上展示他们的研究成果，以提高他们的教育经验。此外，将组织一次关于上述计算技术的专题讨论会。与国家标准与技术研究所合作，将开发一个python脚本版本的模拟软件，并将通过他们的网站传播，用于教育和研究。