Multi-Scale Simulation of Rare-Event Dynamics in Assembly and Catalysis at Surfaces

技术说明：该奖项由材料研究部和化学部共同资助。它支持计算研究和教育，旨在解决使用结构演化模拟来访问长时间和大长度尺度的具有挑战性的问题，同时准确地保留原子细节。分子动力学（MD）模拟可以提供原子尺度上的精确细节。然而，MD在模拟时间或距离上并不实用，远远超出纳米尺度。在许多材料中，动态进化是通过一系列“罕见事件”发生的，在这些事件中，系统在逃逸并转移到另一个势能最小值之前花费了很长一段时间。PI的目标是开发方法来提高当前模拟罕见事件动力学的能力。具体而言，将开发平行动力学蒙特卡罗（KMC）模拟方法，基于晶格的KMC模拟与速率方程的结合，以及热脱附的加速MD模拟。这些方法将被应用于描述多层金属薄膜生长中的模式形成和烷烃分子从固体表面的温度程序化解吸（TPD）。计划中的KMC模拟创新将允许其定量应用于多尺度问题，其中长度和时间尺度范围从原子尺度到宏观尺度。这些方法的准确性和效率将被证实和评估。KMC研究将阐明fcc（110）表面金属薄膜外延的模式形成如何取决于表面温度和沉积速率。在这些研究中，确定实验观察到的纳米结构如何自组织以及如何控制自组织是特别有趣的。加速MD研究将是第一个在真实空间中使用MD模拟整个TPD实验的研究。模拟结果将用于解决围绕正构烷烃解吸解释的实验争议。此外，将引入的方法可以使将来模拟大型和复杂的催化系统，其净速率行为反映许多不同的速率过程。非技术说明：该奖项由材料研究部和化学部共同资助。它支持计算研究和教育，旨在解决使用结构演化模拟来捕获长时间和大长度尺度上的基本物理和化学过程的挑战性问题，同时准确地保留原子尺度上的细节。分子动力学模拟可以在原子尺度上提供精确的细节，但对于模拟时间或距离远远超过纳米尺度是不切实际的。在许多物质中，动态进化是通过一系列“罕见事件”发生的。PI旨在开发计算算法和工具，以实现从原子到宏观长度和时间尺度的有意义的模拟。这是计算材料研究和化学中的一个困难而又重要的问题。这个问题的有效解决可以使解释和理解广泛的实验，并有助于发现新的材料和现象。PI将专注于薄膜、图案薄膜和材料表面纳米结构的生长，以及石墨和金属表面链状分子的解吸。从这项工作中产生的算法和计算工具可能会在化学、材料研究和生物物理学的其他领域找到应用。

TECHNICAL EXPLANATION:The Division of Materials Research and the Chemistry Division jointly fund this award. It supports computational research and education aimed at addressing the challenging problem of using simulations of structural evolution to access long-time and large-length scales while accurately retaining atomic detail. Molecular-dynamics (MD) simulations can provide accurate details at the atomic scale. However, MD is not practical for simulating times or distances much beyond the nanometer scale. In many materials, dynamical evolution occurs through a series of "rare events", in which the system spends a long-time period in one potential-energy minimum before escaping and moving on to another. The PI aims to develop methods to advance the current capabilities for simulating rare-event dynamics. Specifically methods will be developed for parallel kinetic Monte Carlo (KMC) simulation, combination of lattice-based KMC simulations with rate equations, and accelerated MD simulation of thermal desorption. These methods will be applied to describe pattern formation in multi-layer, metal thin-film growth and the temperature-programmed desorption (TPD) of nalkane molecules from solid surfaces. Planned innovations to KMC simulations will allow their quantitative application to multi-scale problems, where length and time scales range from atomic scales to macroscopic scales. The accuracy and efficiency of these methods will be confirmed and assessed. The KMC studies will elucidate how pattern formation in metal thin-film epitaxy on fcc(110) surfaces depends on the surface temperature and the deposition rate. It is of particular interest in these studies to determine how experimentally observed nanostructures can self-organize and how the self-organization can be controlled.The accelerated MD studies will be the first studies to simulate an entire TPD experiment in real space with MD. The simulations will be applied to resolve experimental controversy surrounding interpretation of n-alkane desorption. Further, the methodology that will be introduced could enable future simulations of large and complex catalytic systems whose net rate behavior reflects many different rate processes.NON-TECHNICAL EXPLANATION:The Division of Materials Research and the Chemistry Division jointly fund this award. It supports computational research and education aimed at addressing the challenging problem of using simulations of structural evolution to capture essential physical and chemical processes on long-time and large-length scales while accurately retaining detail at the atomic scale. Molecular-dynamics simulations can provide accurate details at the atomic scale, but it is not practical for simulating times or distances much beyond the nanometer scale. In many materials, dynamical evolution occurs through a series of "rare events." The PI aims to develop computational algorithms and tools to enable meaningful simulation that can span from atomic to macroscopic length and time scales. This is a difficult and important problem in computational materials research and chemisty. Effective solutions of this problem can enable the interpretation and understanding of a wide range of experiments and contribute to the discovery of new materials and phenomena. The PI will focus on applications to the growth of films, patterned films, and nanostructures on the surfaces of materials, and the desorption of chainlike molecules from graphite and metal surfaces. The algorithms and computational tools that result from this work may find applications across other areas of chemistry, materials research, and biological physics.