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

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确控制，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者中尺度材料的下一次革命，用于包括信息系统、传感、致动和人工智能在内的多种应用。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。该项目将通过实施一条双重装配线来开发一家自组装铸造厂，在该生产线上同时部署实验、分子模拟和人工智能技术，以创建设计者三维(3D)纳米结构系统。这项工作将检验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连纳米系统到设计用于传感和驱动的机械纳米结构。将特别强调利用拓扑原理开发新的制造路线。该项目的更广泛影响是为培养能够有效地与工业界和学术界接触的新一代科学家或工程师奠定基础。该项目还将促进对社区大学生的培训，开发在线学习材料，以及开展公众参与活动。学术界和产业界的融合；理论、计算和实验；不同的指导观点；自组装制造过程的高级观点，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，重点是培训和知识转移。这个项目将解决的科学挑战是能够使用嵌段共聚物创建任意的3D结构，嵌段共聚物是由两个或更多化学成分组成的聚合物。使用最先进的计算技术并结合实验，该项目旨在从衬底中包含的信息中解开自组装逐层系统的设计规则，这些系统具有预先设计的、非对称的和复杂的3D结构。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并不清楚。容错是主要的挑战之一，将寻求拓扑结构来使自组装过程健壮。通过一种新的双自组装线概念直接学习，这种概念在一个分支上结合了物理过程，在另一个分支上结合了虚拟过程。这两条装配线都将通过人工智能引擎连接起来，以发现隐藏的关联并学习设计规则。因此，该项目有可能为纳米制造的未来制定蓝图。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.