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。它支持旨在解决使用结构演化模拟来获取长时间和大长度尺度，同时准确地保留原子细节这一挑战性问题的计算研究和教育。分子动力学(MD)模拟可以在原子尺度上提供准确的细节。然而，MD对于模拟远远超出纳米尺度的时间或距离是不现实的。在许多材料中，动力学演化是通过一系列“罕见事件”发生的，即系统在逃逸并转移到另一个势能极值之前，在一个势能极小值中花费很长一段时间。PI旨在开发方法，以提高当前模拟罕见事件动态的能力。具体来说，将开发并行动力学蒙特卡罗(KMC)模拟、基于格子的KMC模拟与速率方程相结合的方法以及热脱附的加速MD模拟。这些方法将被用来描述多层金属薄膜生长中的图案形成和N烷分子从固体表面的程序升温脱附(TPD)。对KMC模拟的计划创新将允许它们定量地应用于多尺度问题，其中长度和时间尺度从原子尺度到宏观尺度。这些方法的准确性和效率将得到确认和评估。KMC研究将阐明fcc(110)表面金属薄膜外延中图形的形成如何依赖于表面温度和沉积速率。在这些研究中，人们特别感兴趣的是确定实验观察到的纳米结构如何自组织以及如何控制这种自组织。加速的MD研究将是第一个在真实空间模拟整个具有MD的TPD实验的研究。这些模拟将用于解决围绕正构烷烃解吸解释的实验争议。此外，即将引入的方法可能使未来能够模拟大型和复杂的催化系统，其净速率行为反映了许多不同的速率过程。非技术解释：材料研究部和化学部共同资助该奖项。它支持旨在解决具有挑战性的问题的计算研究和教育，即使用结构演化模拟来捕捉长时间和大长度尺度上的基本物理和化学过程，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准确的细节，但对于模拟远远超出纳米尺度的时间或距离来说，这是不现实的。在许多物质中，动力学演化是通过一系列“稀有事件”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难点和重要问题。这个问题的有效解决可以使人们能够解释和理解广泛的实验，并有助于发现新的材料和现象。PI将侧重于在材料表面生长薄膜、图案化薄膜和纳米结构以及从石墨和金属表面解吸链状分子方面的应用。这项工作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得到应用。

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.