BRITE Fellow: AI-Enabled Discovery and Design of Programmable Material Systems

BRITE 研究员：人工智能支持的可编程材料系统的发现和设计

• 批准号: 2227641
• 负责人: Wei Chen
• 金额: $ 99.98万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2027-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2227641&HistoricalAwards=false
• 关键词: BRITE; Fellow; AI; Enabled; Discovery

This Boosting Research Ideas for Transformative and Equitable Advances in Engineering (BRITE) Fellow grant will establish a transformative data-driven design framework enabled by artificial intelligence (AI) for the real-time digital design and fabrication of programmable material systems (PMS). PMS are emerging architectural structures made of smart materials that are responsive to external stimuli (e.g., stress, thermal inputs, chemical changes, light, and magnetic fields) and that can be programmed to transform between multiple functional states. PMS have far-reaching, societally impactful applications, including surgical robots, (bio)sensors, deployable satellites, mechanical computing, and water and energy harvesting. The design of PMS is still in its infancy, however, due to the complex underlying physics and high dimensionality associated with the design of spatially varying materials, architectures, and stimuli. To address these challenges, this project seeks to integrate disruptive technologies across the multidisciplinary domains of design, mechanics, manufacturing, materials, and data science to create a new AI-enabled PMS digital design platform. In collaboration with Minority Serving Institutions (MSIs), research results will be integrated into AI literacy programs and activities for K-12 and college students. A wide range of diversity, equity, and inclusion activities will also be accomplished, with emphasis on mentoring and collaboration with junior faculty from underrepresented groups and enhancing access to STEM pathways for underrepresented minority students.The research objective of this project is to establish a novel data-driven design framework called ALGO (Acquire-Learn-Generate-Optimize) that will accelerate the co-design of materials (M), architectures (A), and stimuli (S) in programmable material systems (PMS). The specific goals are to: 1) Create a shared PMS data resource to bridge knowledge gaps across multiple disciplines and domains; 2) Develop novel statistical and AI-based learning techniques to understand complex M-A-S interactions and derive transferrable PMS design rules; and 3) Employ a “building block” approach to create multiscale design strategies that combine machine learning with topology optimization to achieve superior computational efficiency and unprecedented performance for real-time PMS digital design. This research will provide a paradigm shift that transforms existing techniques limited to the design of single-material periodic structures into scalable data-driven design of programmable multi-material systems with heterogenous materials and topological architectures. While the PMS design testbeds used in this research will be focused on Shape Transformation, Wave Guiding, and Surface Engineering, the AI-enhanced learning and design automation techniques developed here will benefit a wide range of physics-driven science and engineering domains. Exploiting heterogeneity and programmability in material systems through intelligent design will have long-lasting impacts on US competitiveness in developing innovative, lightweight, portable, economic, and sustainable products.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.

这一促进工程（BRITE）赠款方面的变革性和公平进步的研究思想将建立一个由人工智能（AI）实现的变革性数据驱动的设计框架，用于实时数字设计和可编程材料系统（PMS）的制造。 PMS是由智能材料制成的新兴建筑结构，这些结构对外部刺激有反应（例如，压力，热输入，化学变化，光和磁场），可以编程以在多个功能状态之间进行编程。 PMS具有深远的社会影响力应用，包括手术机器人，（BIO）传感器，可部署的卫星，机械计算以及水和能量收集。但是，由于与空间变化的材料，架构和刺激的设计相关的复杂的基础物理学和高维度，因此PMS的设计仍处于起步阶段。应对这些挑战，该项目旨在整合设计，力学，制造，材料和数据科学的多学科领域的破坏性技术，以创建一个新的AI支持AI支持AI支持的PMS数字设计平台。与少数民族服务机构（MSIS）合作，研究结果将纳入K-12和大学生的AI扫盲计划和活动中。还将完成广泛的多样性，公平和包容活动，重点是与代表性不足的群体的初级教师进行心理化和协作，并增强了该项目的研究目标不足的研究目标。 （a）和可编程材料系统（PMS）中的刺激。具体目标是：1）创建共享的PMS数据资源，以弥合多个学科和域的知识差距； 2）开发新颖的统计和基于AI的学习技术，以了解复杂的M-A-S相互作用并得出转移的PMS设计规则； 3）采用一种“构件”方法来创建多种设计策略，将机器学习与拓扑优化相结合，以实现实时PMS数字设计的卓越计算效率和前所未有的性能。这项研究将提供一个范式转变，将现有技术限于单物质周期性结构的设计转换为具有异源材料和拓扑结构的可编程多物质系统的可扩展数据驱动的设计。尽管本研究中使用的PMS设计测试台将集中在形状转化，波浪指南和表面工程上，但此处开发的AI增强学习和设计自动化技术将使广泛的物理驱动的科学和工程领域受益。通过智能设计利用材料系统中的异质性和可编程性将对美国在开发创新，轻巧，便携式，经济和可持续产品方面的竞争力产生长期影响。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛的影响来评估的支持，并被认为是值得的。