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

加州大学圣巴巴拉分校的M. Scott Shell获得了化学学部化学理论、模型和计算方法项目的奖励，为系统粗粒度分析开发了基础广泛的多尺度算法。该项目由化学、生物工程、环境和运输系统部（CBET）的界面过程和热力学项目共同资助。理论思想和数值技术的重大进展对于实现许多大长度和时间尺度问题的定量建模至关重要。这项工作利用了一种新的理论概念，称为相对熵，它量化了粗粒度过程中丢失的信息，并为多尺度问题提供了一种通用的统计力学方法。该PI使用该框架创建健壮的算法，提供新的粗粒度范例，包括处理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博士还使用新一代技术来模拟和理解二苯丙氨酸肽进入空心纳米管的早期结构、热力学和分子机制。该项目与针对一系列学生的几项教育活动紧密结合。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模型，这种模型可以在极大地减少计算需求的同时删除一小部分原子细节。如果一个人能够通过历史上的试错或理性的洞察，找到粗粮的“正确”方法，这个策略就会成功。Shell博士引入了一种新的方法，通过量化和最小化粗粒度导致的信息损失，以一种物理上深刻的方式自动化这一过程。他的团队正在开发强大的新通用算法，生成优化和精确的粗粒度模型，使模拟复杂系统的复杂程度达到新的水平。特别是，他正在使用这些方法来理解最近发现的一种设计肽，这种设计肽可以自组装成空心纳米管，在纳米材料和能源领域的应用正在迅速增加。

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.