SNM: Manufacturing Autonomy for Directed Evolution of Materials (MADE-Materials) for Robust, Scalable Nanomanufacturing

尖端材料的开发和制造通常涉及耗时的材料和工艺设计阶段，然后随着制造规模从实验室、中试计划到工业工艺规模，对样品进行广泛的测试以调整工艺条件。这些不同的步骤提高了将新的和改进的材料引入工业管道的成本障碍，并增加了国内制造的先进纳米材料的成本。幸运的是，数值模拟、添加剂制造和材料快速测试的最新发展表明，材料开发和纳米制造的新方法，以前不同和耗时的阶段可以几乎瞬间进行，以获得最优的材料结构和制造工艺条件。该奖项的重点是通过以下方式改造传统的纳米结构材料开环合成：1)使用多功能3D打印方法来制造这些纳米材料和纳米结构；2)将材料特性表征直接纳入打印过程；以及3)使用人工智能(AI)算法来动态调整工艺条件，以实现所需的材料性能。这些概念和组成部分将被整合到技术课程、实践研究机会以及面向广泛学生和公众的外联研讨会中。联合PIS计划通过其小组与当地博物馆的合作，以及课程和课外活动，利用现有的外联和教育活动。该方法和框架是对过程建模、材料合成和表征以及系统设计的研究，以自主发现新的材料配置并减少制造缺陷和不确定性。这一人工智能框架将“理解”工艺-结构-性能关系、制造约束，以及重要的材料特性和制造质量的统计变化。这项研究的智力价值在于发现了具有监督遗传算法的通用纳米制造工具和原料，它们通过寻找替代设计来自动纠正缺陷并弥补固有的制造不准确；这与将不确定性降至最低(例如环境控制)或依赖人工干预的制造后表征的标准工具形成了鲜明对比。该框架将使用纳米级添加剂制造(AM)作为基本制造工具，并使用纳米结构超材料作为应用程序进行测试。通过这种方法制造的范例和纳米级超材料对集成系统的可伸缩纳米制造具有深远的影响。自动演化参数以满足构造规范的系统范例可扩展到大规模添加剂制造和制药，在这些领域，可用的工艺参数空间和化学成分非常庞大，而且设计并不直观。添加纳米制造技术具有改变超材料设计的潜力，它可以用多种材料进行3维(3D)设计，创建复杂的复合亚结构。

The development and manufacturing of cutting edge materials typically involves time-consuming materials and process design phases, followed by extensive testing of samples to adjust process conditions as the manufacturing scales up from the lab, to pilot plan, to industrial process scale. These distinct steps drive up the cost barrier to introduction of new and improved materials into the industrial pipeline and increase the cost of domestically manufactured advanced nanomaterials. Fortunately, recent developments in numerical modeling, additive manufacturing, and rapid testing of materials suggest that a new approach to material development and nanomanufacturing, where the previously distinct and time-consuming phases could be carried out nearly instantaneously to arrive at optimal material structure as well as process conditions for its manufacture. The focus of this award is to revamp the traditional, open-loop synthesis of nanostructured materials by: 1) using a versatile 3-D printing approach to manufacture these nanomaterials and nanostructures, 2) incorporate material property characterization directly into the printing process, and 3) use an artificial intelligence (AI) algorithm to adjust on the fly process conditions to achieve desired material properties. These concepts and components will be integrated into technical coursework, hands-on research opportunities, and outreach workshops to a broad range of students and the public. The co-PIs plan to leverage existing outreach and educational activities through their group's collaboration with a local museum, as well as curricular and extracurricular activities. The approach and framework is an investigation of process modeling, materials synthesis and characterization, and system design to autonomously discover new material configurations and reduce manufacturing defects and uncertainty. This AI framework will "understand" process-structure-property relationships, manufacturing constraints, and, importantly, statistical variations in material properties and manufacturing quality. The intellectual merit of this study is the discovery of general nanomanufacturing tools and feedstocks, with supervisory genetic algorithms, that autonomously correct for defects and compensate for innate manufacturing inaccuracies by a search for alternative designs; this is in contrast to standard tools that minimize uncertainty (e.g. environmental controls) or rely on post-fabrication characterization with human intervention. The framework will be tested using nanoscale additive manufacturing (AM) as the fundamental manufacturing tool and nanostructured metamaterials as the application. The paradigm and nanoscale metamaterials made via this approach have far-reaching impacts on scalable nanomanufacturing for integrated systems. The paradigm of systems that autonomously evolve parameters to meet construct specifications is extensible to macroscale additive manufacturing and pharmaceuticals where the process parameter space and chemistries available is vast, and design is not intuitive. Additive nanomanufacturing has the potential to transform metamaterial design by enabling design in 3-dimensions (3D) with multiple materials, creating complex composite metastructures.