New Concepts and Algorithms for Coarse-Graining in Self-Assembling Systems

M.加州大学圣巴巴拉分校的Scott Shell获得了化学系化学理论、模型和计算方法项目的奖项支持，为系统粗粒度化开发基础广泛的多尺度算法。 该项目由化学，生物工程，环境和运输系统（CBET）部门的界面过程和热力学计划共同资助。 理论思想和数值技术的重大进展是必不可少的，使许多大的长度和时间尺度的问题的定量建模。这项工作利用了一个新的理论概念，称为相对熵，量化在粗粒化过程中丢失的信息，并提供了一个通用的统计力学方法来解决多尺度问题。 这个PI使用这个框架来创建强大的算法，提供新的粗粒度范例，包括解决高复杂性多参数模型，多体交互，可转移性和CG架构设计的能力。 这些算法正在被实现到一个免费的、社区开发的粗粒度包中。 Shell博士还使用这种新一代技术来建模和理解控制二苯基丙氨酸肽自组装成中空纳米管的早期结构、热力学和分子机制。 该项目正与针对一系列学生的若干教育活动密切结合。生物分子、材料和许多其他系统的原子分辨率模拟提供了对其行为的重要见解和对其性质的有用预测。 然而，这种方法受到计算费用的严重限制，只能用于最小和最简单的系统。 为了将建模扩展到更复杂的情况，长期以来一直习惯于追求粗粒度的模型，这些模型去除了一部分原子细节，同时大大降低了计算需求。 如果人们能够通过试错法或理性的洞察力来识别粗粒度的“正确”方法，那么这种策略是成功的。 Shell博士介绍了一种新方法，通过量化和最小化粗粒度化导致的信息损失，以物理上有洞察力的方式自动化这一过程。 他的团队正在开发强大的新的通用算法，这些算法可以生成优化和精确的粗粒度模型，从而使模拟复杂系统的复杂性达到新的水平。 特别是，他正在使用这些方法来了解最近发现的设计师肽，该肽自组装成中空纳米管，在纳米材料和能源方面的应用迅速增长。

M. Scott Shell of the University of California Santa Barbara is supported by an award from the Chemical Theory, Models, and Computational Methods program in the Chemistry division to develop broad-based multiscale algorithms for systematic coarse-graining. The project is cofunded by the Interfacial Processes and Thermodynamics program in the Division of Chemical, Bioengineering, Environmental, and Transport Systems (CBET). Major advances in theoretical ideas and numerical techniques are essential to enabling quantitative modeling of many large length and time scale problems. This work leverages a new theoretical concept called the relative entropy that quantifies the information lost during coarse-graining and that provides a general statistical mechanical approach to multiscale problems. This PI is using this framework to create robust algorithms that offer new coarse-graining paradigms, including the ability to address high-complexity many-parameter models, multi-body interactions, transferability, and design of CG architectures. These algorithms are being implemented into a freely-available, community-developed coarse-graining package. Dr. Shell is also using this new generation of techniques to model and understand the early structures, thermodynamics, and molecular mechanisms governing the self-assembly of the diphenylalanine peptide into hollow nanotubes. The project is being closely integrated with several educational activities targeting a range of students. Atomic-resolution simulations of biological molecules, materials, and many other systems offer major insights into their behavior and useful predictions of their properties. However, such approaches are severely limited by computational expense to the smallest and simplest of systems. To extend modeling to more complicated cases, it has long been customary to pursue coarse-grained models that remove a fraction of atomic detail while greatly reducing the computational demands. This strategy can be successful if one can identify, historically by trial-and-error or rational insight, the "right" way to coarse-grain. Dr. Shell has introduced a new approach that automates this process in a physically insightful way, by quantifying and minimizing the information loss due to coarse-graining. His group is developing powerful new and general algorithms that produce optimized and accurate coarse-grained models, enabling a new level of sophistication in simulating complex systems. In particular, he is using these approaches to understand a recently discovered designer peptide that self-assembles into hollow nanotubes with a fast-growing list of applications in nanomaterials and energy.