ITR: Mesoscopic Modeling and Simulation: A Novel Approach to Monte Carlo Methods

提案编号0219211 PI：Markos A. Katsoulakis机构：马萨诸塞州阿默斯特大学题目：ITR：介观建模与仿真：一种新的蒙特卡罗方法摘要该项目关注的是一种新的框架，蒙特卡罗模拟的基础上，最近开发的粗粒度随机介观模型。 介观模型是随机偏微分方程，其通过非平衡统计力学的技术从微观蒙特卡罗（MC）算法严格导出为渐近极限。 尽管这些模型描述的是比潜在的分子尺度大得多的介观尺度，但它们仍然包括关于粒子相互作用和动力学的详细微观信息，并且可以系统地模拟各向异性和多种微观机制。 介观模型的另一个有吸引力的特点是包含随机波动直接从基本的主方程，并产生重要的成核和图案的形成和选择机制。 最后，正式和严格的渐近使用大偏差和WKB展开以及初步的数值模拟表明，MC算法和相应的随机介观模型产生基本相同的结果，如成核率和相变等微妙的数量。 本提案的主要研究目标是：（a）利用高效的基于谱的方法数值求解随机介观方程，开发非平衡粗粒度MC算法，并与传统MC进行详细的基准测试，和（B）将所提出的计算工具应用于先进材料中的图案形成和纳米多孔膜中的分子分离。由于其基本原理，分子动力学模拟和蒙特卡罗（MC）算法在描述原子和分子之间复杂的非平衡相互作用方面的广泛性和多样性已经成为科学和工程研究的卓越计算工具。 随着计算能力的增强，这些方法可以为从物理化学和生物过程到生物材料、药物设计、模式识别和图像处理等众多问题提供前所未有的见解。 尽管它们的广泛使用和相关的计算方法的实质性进展，分子算法是有限的短长度和时间尺度。 因此，它们只能在很短的时间内模拟相对少量的原子或分子。 另一方面，在实验中观察到的设备尺寸和形态特征通常涉及更大的空间和/或时间尺度。 在满足这一多尺度建模挑战的一个主要障碍是缺乏一个严格的数学和计算框架，提供一个直接的联系，从原子尺度的复杂的介观和宏观现象，微观相互作用的结果。 在这个方向上，我们的工作重点是开发新的随机模型和算法，能够描述更大的长度和时间尺度比传统的MC模拟，同时仍然结合微观细节。 我们打算应用计算方法来提供新的见解到两个工程问题，这是目前棘手的与传统的MC技术：（1）自组织微观机制的研究及其在先进材料的图案形成中的作用，和（2）在纳米多孔薄膜和膜的分子的运输和分离。

Proposal # 0219211PI: Markos A. KatsoulakisInstitution: University of Massachusetts AmherstTitle: ITR: Mesoscopic Modeling and Simulation: A Novel Approach to Monte CarloABSTRACTThis project is concerned with a novel framework for Monte Carlo simulations based on recently developed coarse-grained stochastic mesoscopic models. Mesoscopic models are stochastic partial differential equations which are rigorously derived as asymptotic limits from microscopic Monte Carlo (MC) algorithms by means of techniques from non-equilibrium statistical mechanics. Although such models describe the mesoscopic scales, which are much larger than the underlying molecular scales, they still include detailed microscopic information on particle interactions and dynamics and can systematically model anisotropies and multiple micromechanisms. Another attractive feature of mesoscopic models is the inclusion of random fluctuations derived directly from the underlying master equation and yielding important nucleation and pattern formation and selection mechanisms. Finally, formal and rigorous asymptotics using Large Deviation and WKB expansions as well as preliminary numerical simulations indicate that the MC algorithms and the corresponding stochastic mesoscopic models produce essentially identical results for such delicate quantities as nucleation rates and phase transitions. The main research objectives of this proposal are: (a) to develop non-equilibrium coarse-grained MC algorithms by numerically solving the stochastic mesoscopic equations using highly efficient spectral-based methods and carry out detailed benchmarkings against conventional MC, and (b) to apply the proposed computational tools to applications arising in pattern formation in advanced materials and molecular separation in nanoporous films.Because of their fundamental nature and their versatility in describing complex out-of-equilibrium interactions between atoms and molecules, molecular dynamics simulations and Monte Carlo (MC) algorithms have become preeminent computational tools for science and engineering research. With the advent of enhanced computing capabilities, these methods can provide unprecedented insights into numerous problems ranging from physicochemical and biological processes to biomaterials, drug design, pattern recognition, and image processing. Despite their widespread use and the substantial progress in related computational methods, molecular algorithms are limited to short length and time scales. Hence, they are capable of simulating only a relatively small number of atoms or molecules for quite short time periods. On the other hand, device sizes and morphological features observed in experiments often involve much larger spatial and/or temporal scales. A major obstacle in meeting this multiscale modeling challenge is the lack of a rigorous mathematical and computational framework providing a direct link from the atomistic scale to the complex mesoscopic and macroscopic phenomena that are the result of the microscopic interactions. In this direction, our work focuses on developing novel stochastic models and algorithms capable of describing much larger length and time scales than conventional MC simulations while still incorporating microscopic details. We intend to apply the computational methods to provide new insights into two engineering problems, which are currently intractable with conventional MC techniques: (1) the study of self-organizing micromechanisms and their role in pattern formation in advanced materials, and (2) the transport and separation of molecules in nanoporous films and membranes.