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

这项促进工程变革和公平进步的研究思路（BRITE）研究员资助将建立一个由人工智能（AI）支持的变革性数据驱动设计框架，用于可编程材料系统（PMS）的实时数字设计和制造。PMS是一种新兴的建筑结构，由智能材料制成，可以响应外部刺激（例如，应力、热输入、化学变化、光和磁场），并且可以在多种功能状态之间进行编程转换。PMS具有深远的、具有社会影响力的应用，包括手术机器人、（生物）传感器、可部署卫星、机械计算以及水和能源收集。然而，由于复杂的底层物理和与空间变化材料、结构和刺激设计相关的高维性，PMS的设计仍处于起步阶段。为了应对这些挑战，该项目寻求整合设计、机械、制造、材料和数据科学等多学科领域的颠覆性技术，以创建一个新的支持人工智能的PMS数字设计平台。与少数民族服务机构（msi）合作，研究成果将融入K-12和大学生的人工智能扫盲计划和活动中。还将开展广泛的多样性、公平性和包容性活动，重点是与来自代表性不足群体的初级教师进行指导和合作，并为代表性不足的少数族裔学生提供更多接触STEM途径的机会。该项目的研究目标是建立一个名为ALGO（获取-学习-生成-优化）的新型数据驱动设计框架，该框架将加速可编程材料系统（PMS）中材料(M)，架构(a)和刺激(S)的协同设计。具体目标是：1)创建共享的PMS数据资源，以弥合跨学科和领域的知识差距；2)开发新的统计和基于人工智能的学习技术，以理解复杂的M-A-S交互，并推导可转移的PMS设计规则；3)采用“构建块”方法创建多尺度设计策略，将机器学习与拓扑优化相结合，为实时PMS数字设计实现卓越的计算效率和前所未有的性能。这项研究将提供一种范式转变，将现有技术局限于单材料周期性结构的设计，转变为具有异质材料和拓扑结构的可编程多材料系统的可扩展数据驱动设计。虽然本研究中使用的PMS设计测试平台将专注于形状变换、波浪引导和表面工程，但这里开发的人工智能增强学习和设计自动化技术将有利于广泛的物理驱动的科学和工程领域。通过智能设计开发材料系统的异质性和可编程性，将对美国在开发创新、轻便、便携、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。

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.