Collaborative Research: DMREF: Designer 3D Mesoscale Materials Synthesized in the Self-Assembly Foundry

合作研究：DMREF：在自组装铸造厂合成的设计师 3D 介观尺度材料

• 批准号: 2118415
• 负责人: Vincenzo Vitelli
• 金额: $ 60万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118415&HistoricalAwards=false
• 关键词: Collaborative; Research; DMREF; Designer; 3D

Self-assembly is one of the most promising avenues for the manufacturing/synthesis of materials and systems with exquisite control over nanoscale features while being fast, scalable, and inexpensive. It could enable the next revolution in integrated systems and designer mesoscale materials for multiple applications including information systems, sensing, actuation, and artificial intelligence. However, there are still many challenges in utilizing self-assembly as a precision fabrication technique. This project will develop a self-assembly foundry by implementing a dual assembly line in which experimental, molecular simulations, and artificial intelligence techniques are deployed simultaneously to create designer 3-dimensional (3D) nanostructured systems. This effort will examine the limits of 3D self-assembly to accelerate the fabrication of custom systems, from interconnected nanosystems used in computer chips to designer mechanical nanostructures for sensing and actuation. Particular emphasis will be placed in developing new manufacturing routes using topological principles. The broader impacts of this project envision a basis for training a new generation of scientist or engineers that can engage effectively with industry and academia. This project will also lead to the training of community college students and the development of online learning materials, as well as public engagement activities. The convergence of academia and industry; theory, computation, and experiment; different mentoring perspectives; and the high-level view of the self-assembly manufacturing process will provide a rich environment for the participants to develop new knowledge, skills, and abilities, with a strong emphasis on training and knowledge transfer.The scientific challenge that this project will tackle is that of being able to create arbitrary 3D structures using block copolymers, which are polymers composed of two or more chemistries. Using state-of-the-art computational techniques in conjunction with experiments, this project aims to unravel the design rules for self-assembling layer-by-layer systems that have a predesigned, non-symmetric, and intricate 3D structure from the information contained in the substrate. While in the optics field this corresponds to creating a hologram, the rules for doing so in a self-assembly system are not clear. Fault tolerance is one of the main challenges and topological constructs will be sought to make the self-assembly process robust. Direct learning through a new dual self-assembly line concept that combines, on one branch the physical process and in another branch the virtual process, will also be employed. Both of these assembly lines will be connected through an artificial intelligence engine to find the hidden correlations and learn the design rules. Thus, this project has the potential to set a blueprint for the future of nanomanufacturing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

自组装是制造/合成材料和系统的最有前途的途径之一，具有对纳米级特征的精细控制，同时具有快速、可扩展和廉价的特点。它可以实现集成系统和设计中尺度材料的下一次革命，用于多种应用，包括信息系统，传感，驱动和人工智能。然而，利用自组装作为一种精密制造技术仍然存在许多挑战。该项目将通过实施双装配线来开发自组装铸造厂，同时部署实验，分子模拟和人工智能技术来创建设计的三维（3D）纳米结构系统。这项工作将研究3D自组装的局限性，以加速定制系统的制造，从用于计算机芯片的互联纳米系统到设计用于传感和驱动的机械纳米结构。特别强调将放在开发新的制造路线使用拓扑学原理。该项目的广泛影响为培养新一代科学家或工程师提供了基础，这些科学家或工程师可以有效地与工业界和学术界合作。该项目还将培训社区大学生，开发在线学习材料，以及开展公众参与活动。学术界与产业界的融合；理论、计算和实验；不同的指导观点；而自组装制造过程的高层视图将为参与者提供一个丰富的环境来发展新的知识、技能和能力，并强调培训和知识转移。该项目将解决的科学挑战是能够使用嵌段共聚物创建任意3D结构，嵌段共聚物是由两种或多种化学物质组成的聚合物。利用最先进的计算技术与实验相结合，该项目旨在揭示自组装层接层系统的设计规则，这些系统具有预先设计的、非对称的、复杂的3D结构，这些结构来自基板中包含的信息。虽然在光学领域，这相当于创建全息图，但在自组装系统中这样做的规则尚不清楚。容错是其中一个主要挑战，需要寻求拓扑结构来使自组装过程具有鲁棒性。通过新的双重自组装线概念进行直接学习，在一个分支上结合物理过程，在另一个分支上结合虚拟过程。这两条装配线将通过人工智能引擎连接起来，以发现隐藏的相关性并学习设计规则。因此，这个项目有可能为未来的纳米制造制定蓝图。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。