CDS&E: Toward a Pattern Recognition Framework to Identify Reaction Coordinates for Order-Disorder Transitions: Application to Block Copolymers

CDS

• 批准号: 1609997
• 负责人: Fernando Escobedo
• 金额: $ 30万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1609997&HistoricalAwards=false
• 关键词: CDS& Toward; Pattern; Recognition; Framework

NONTECHNICAL ABSTRACTThis award supports computational research and education to develop a new method to identify the degree of organization contained in an arrangement of atoms or molecules as it changes from a disorganized or disordered arrangement to a more organized or ordered arrangement, particularly in computer simulations of materials. In the processing of many materials, from soft materials made of long chain-like molecules, or polymers, to inorganic solids like metal alloys, transformations often occur that involve changes in the nature of the way atoms or molecules self-organize from a more disordered arrangement to a more ordered arrangement. Such changes in internal order typically translate into substantial changes in the properties of the material. Among the many promising building blocks available to generate desirable ordered structures, block copolymers, a class of polymers, stand out because a variety of materials can be made from them with highly regular patterns and pores of nanoscale dimensions, for potential applications in the making of semiconductor films, filters, and conducting membranes. However, the realization of such highly regular structures is difficult and often involves more art than science. Paramount to understanding and controlling microscopic ordering is to have good "order parameters," quantities that researchers can use to monitor the progress towards the desired well organized or ordered state. Such order parameters distill a huge amount of microscopic and atomic-scale data into just a few crucial quantities; currently, they are developed in a case-by-case fashion, often involving significant computational effort and trial and error. In this project, the PI aims to advance a framework to help identify useful order parameters based on ideas from the area of pattern recognition. Just like effective computer programs have been developed and deployed for face recognition, for example, the PI aims to develop techniques for describing the key geometrical signatures that accompany the onset and propagation of microscopic order. The close collaboration of the PI with experimental groups will ensure a synergy between computational and experimental efforts. The project provides an environment to train and support a doctoral student and to provide research experience for two undergraduate students, one of whom will develop outreach educational modules. Results from this investigation will also be used in advanced chemical engineering modeling classes taught by the PI.NONTECHNICAL ABSTRACT This award supports computational research and education with the goal to apply and develop pattern recognition (PR) algorithms that would allow users to screen out and identify good descriptors of the transition kinetics from input trajectory data containing a large number of snapshots of configuration files of the system as it goes from one state of order to another. In particular, this proposal seeks pattern-recognition-based reaction coordinates to track the kinetics of order-disorder phase transitions involved in block copolymer systems, emphasizing the not-well understood processes that form bicontinuous phases like gyroid, double diamond, and plumbers nightmare, all made of periodic interweaving 3D networks. The PI envisions that by identifying appropriate reaction coordinates discovered in this way, a more complete and intuitive understanding will be attained of the ordering process and how the system could be steered into alternative pathways that lead to different stable and metastable states.The PI plans to execute the following activities: (a) Determine the conditions at which sought after bicontinuous phases form, a step entailing free energy simulations to discriminate stable from metastable phases and generation of the reference ordered structures of interest. (b) Simulate transition-path ensemble data for varying supercooling conditions. (c) Develop candidate pattern-recognition-based descriptors from the phases simulated in step (a), and assess their quality based on data collected in (b). (d) Apply the best reaction coordinate thus found to gain physical insight into the kinetics of the process and phase bifurcation. This investigation will provide pattern-recognition algorithms and pattern-recognition-based descriptors proven to be helpful in describing the formation kinetics of geometrically complex ordered phases. The method can find numerous new applications, including kinetic studies of thermal, laser and solvent annealing, and directed assembly of block copolymer thin films on patterned substrates.

该奖项支持计算研究和教育，以开发一种新的方法，在原子或分子的排列从无序或无序的排列转变为更有组织或有序的排列时，识别它所包含的组织程度，特别是在材料的计算机模拟中。在许多材料的加工过程中，从由长链状分子或聚合物制成的软材料，到像金属合金这样的无机固体，经常发生变化，涉及原子或分子自组织的性质从更无序的排列到更有序的排列的变化。这种内部秩序的变化通常会转化为材料性质的实质性变化。在许多可用于产生理想有序结构的构建块中，嵌段共聚物是一类突出的聚合物，因为由它们可以制造出具有高度规则的图案和纳米尺度的孔的各种材料，在制造半导体薄膜、滤光片和导电膜方面具有潜在的应用。然而，实现这种高度规则的结构是困难的，而且往往涉及更多的艺术而不是科学。要理解和控制微观有序，最重要的是要有好的“有序参数”，研究人员可以使用这些量来监控朝着所需的良好组织或有序状态的进展。这种序参数将大量的微观和原子尺度的数据提炼成几个关键的量；目前，它们是以个案的方式开发的，通常涉及大量的计算工作和反复尝试。在这个项目中，PI旨在提出一个框架，以帮助识别基于模式识别领域的想法的有用的顺序参数。例如，就像已经为人脸识别开发和部署了有效的计算机程序一样，PI的目标是开发描述伴随着微观秩序的开始和传播的关键几何特征的技术。PI与实验小组的密切合作将确保计算和实验工作之间的协同。该项目提供了一个环境来培训和支持一名博士生，并为两名本科生提供研究经验，其中一人将开发推广教育模块。这项研究的结果也将用于PI.NONTECHNICAL摘要教授的高级化学工程建模课程。该奖项支持计算研究和教育，目标是应用和开发模式识别(PR)算法，允许用户从输入轨迹数据中筛选出并识别良好的转变动力学描述符，该数据包含系统从一种有序状态到另一种有序状态的配置文件的大量快照。特别是，这一建议寻求基于模式识别的反应坐标来跟踪嵌段共聚体系中涉及的有序-无序相变的动力学，强调形成双连续相的不被很好理解的过程，如陀螺体、双钻石和管道工噩梦，所有这些都由周期性交织的3D网络组成。PI设想，通过确定以这种方式发现的合适的反应坐标，将获得对有序化过程以及如何引导系统进入导致不同稳定和亚稳态的替代路径的更完整和直观的理解。PI计划执行以下活动：(A)确定所寻求的双连续相形成的条件，这一步骤需要自由能模拟来区分稳定相和亚稳相，并产生感兴趣的参考有序结构。(B)模拟不同过冷条件下的过渡路径集合数据。(C)根据步骤(A)中模拟的阶段制定基于模式识别的候选描述符，并根据(B)中收集的数据评估其质量。(D)应用由此发现的最佳反应坐标，以获得对该过程和相分叉的动力学的物理洞察。这项研究将提供模式识别算法和基于模式识别的描述符，它们被证明有助于描述几何复杂有序相的形成动力学。该方法可以发现许多新的应用，包括热、激光和溶剂退火的动力学研究，以及在图案化的衬底上定向组装嵌段共聚聚合物薄膜。