Theory and Application of Coarse Graining

粗粒度理论与应用

• 批准号: RGPIN-2021-03852
• 负责人: Thachuk, Mark
• 金额: $ 1.75万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=748121
• 关键词: Theory; Application; Coarse; Graining

Computer simulation is a foundational tool used to advance knowledge and understanding in virtually every field of scientific endeavour, from describing enzyme catalysis or the behaviour of complex lipid membranes in biology, to the motion of planets and galaxies in astrophysics. However some processes, such as changes in polymer configurations under sheared flows or the structural failure of materials, span sufficiently large length and time scales to be inaccessible with fully atomistic simulation using state-of-the-art computers. Coarse graining (CG) is a technique for treating such systems that reduces the effective number of degrees of freedom while retaining the correct dynamical and statistical properties. My long-term goals are: i) to increase by several orders of magnitude in space and time the systems that can be treated with simulation methodologies, and ii) to make simulation accurate enough to completely replace experiment.  These goals can be reached by advancing the theory and application of CG methods and by constructing true, many-body potentials. With funding for 3 PhD students, 1 PDF, and several undergraduate students, this proposal has short-term goals focused on the following three themes: CG Solvent Models Consider a CG scheme that groups solvent molecules into cubes resembling the fluid elements used in continuum mechanics. These objects are coarser than molecular but finer than continuum and can bridge these two limits in a physically correct manner. They can be used to replace solvent molecules in atomistic simulations, leading to significant speedup, or be paired with a hydrodynamic simulation to produce a self-consistent multiscale simulation methodology. A multiscale methodology is required for describing complex materials (like nanoparticles embedded in polymer matrices) or complex fluid flows (like flows through carbon nanotube filters).  This proposal seeks to build a physically correct CG solvent. Combining Atomistic and CG Simulations The CG theory developed in my group allows for the correct formulation of hybrid simulation methods incorporating CG "particles" with atomistic ones. This is the key to expanding the time and length scales of simulations by allowing some parts of the system to be treated with atomic resolution, and less important parts with a CG description.  This proposal will develop such hybrid methods with the complete dynamics, including the effects of dissipation and fluctuations. True Many-Body Potentials The ability to simulate a system over a wide range of physical conditions requires the exact potential, which in principle is many-body.  Virtually all simulations today use effective two-body potentials and are thus limited to the regions of phase space in which they are parameterized.  This proposals seeks to use machine learning to help build models of higher-order terms in many-body potentials, starting with atomistic systems, to ultimately replace the need for experiment.

计算机模拟是一种基础工具，用于在科学努力的几乎每个领域中促进知识和理解，从描述生物学中复杂脂质膜的酶催化或复杂脂质膜的行为到天体物理学中的行星和星系的运动。然而，某些过程，例如剪切流下的聚合物构型的变化或材料的结构故障，跨越了足够大的时间和时间尺度，无法使用最先进的计算机完全原子模拟。粗粒（CG）是一种处理此类系统的技术，可在保留正确的动态和统计特性的同时减少有效的自由度。我的长期目标是：i）在空间和时间上增加几个数量级，可以用模拟方法处理的系统，ii）使模拟变得足够准确以完全替代实验。可以通过推进CG方法的理论和应用以及构建真实的多体潜力来实现这些目标。该提案的资金为3名博士学位学生，1名PDF和几位本科生的资金，其短期目标的重点是以下三个主题：CG溶剂模型考虑了一种CG方案，该方案将溶剂分子分组为类似于连续机械中使用的流体元素的立方体。这些物体比分子更粗糙，但比连续体更细，可以以物理正确的方式桥接这两个限制。它们可用于替代原子模拟中的溶液分子，从而导致显着的加速，或与流体动力模拟配对以产生自洽的多尺度模拟方法。描述复杂材料（例如嵌入聚合物材料中的纳米颗粒）或复杂的流体流（例如流过碳纳米管过滤器）所需的多尺度方法。该建议旨在建立一个身体上正确的CG解决方案。结合原子和CG仿真我组中开发的CG理论允许使用编码CG“颗粒”与原子化的混合模拟方法的正确公式。这是通过允许使用原子分辨率处理系统的某些部分以及具有CG描述的不太重要的部分来扩展模拟时间和长度尺度的关键。该建议将开发出具有完整动力学的混合方法，包括耗散和波动的影响。真正的多体潜力能够在各种物理条件下模拟系统的能力，需要确切的潜力，从原则上讲，这是多体的。如今，几乎所有模拟都使用有效的两体势，因此仅限于参数化的相空间区域。该建议旨在使用机器学习来帮助建立从原子系统开始的多体势中的高阶术语模型，以最终取代实验的需求。