DMREF/Collaborative Research: Inverse Design of Architected Materials with Prescribed Behaviors via Graph Based Networks and Additive Manufacturing

材料在动力载荷作用下的力-位移响应、模态响应以及波的传输和吸收响应，都可以被解释为材料的特征指纹。由于材料微观结构、几何形状和外加载荷之间复杂的非线性相互作用，材料在不到一秒的动态载荷作用下的行为仍然知之甚少。复杂性增加了建筑材料的流形，其中拓扑考虑对于实现特定的响应或功能至关重要。因此，有系统地设计具有最佳动态指纹的建筑材料是一个尚未得到充分解决的挑战。通过无缝集成图网络理论、机器学习、数值模拟和高速增材制造方法的进步，该设计材料革命性和工程化我们的未来（DMREF）奖将加速具有可定制动态指纹的建筑材料的理解、逆向设计和制造。结果将是通过桌面增材制造制造具有规定行为（如冲击屏蔽和波传输）的具有反向设计的三维微结构的材料。应用包括能量和冲击吸收，声波滤波，可拉伸电子，和其他多功能材料系统。该项目还将培训研究生和本科生基于期望行为的自主逆向设计和增材制造的新范式。此外，示范模块、设计游戏和增材打印活动将用于向K-12学生推广。该项目将扩展基于图形的生成机器学习建模技术，以识别建筑材料中的潜在主题，以了解其动态行为，并为优化功能响应提供逆向设计框架。第一步是开发一个图空间模型来表示由任意复杂的3D微架构组成的任意架构材料，包括大小、比例、层次、晶格拓扑和材料属性。下一步是利用大量的低阶实验数据获得高保真度的实验数据和高阶模拟数据，以加速训练和发现过程。基于前向图的机器学习模型将在组合数据上进行功能响应预测训练。最后，基于前向预测模型，使用强化学习的图神经网络生成具有期望属性的图。这个广泛和实验验证的框架将用于发现有关结构和动态特性的基本知识，然后利用这些知识来逆设计具有规定动态指纹的材料。该项目由工程理事会（ENG）的土木、机械和制造创新司（CMMI）和计算机与信息科学与工程理事会（CISE）的信息和智能系统司（IIS）共同资助。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。

A material's force-displacement response, modal response, and wave transmission and absorption response to dynamic loadings, all can be construed as its characteristic fingerprints. The behaviors of materials under dynamic loads that are applied within a fraction of a second remain poorly understood due to the complex, nonlinear interplay between material microstructure, geometry, and applied load. The complexity increases manifold for architected materials, in which topological considerations are paramount to achieve specific responses or functions. Consequently, methodical design of architected materials with optimal dynamic fingerprints is a challenge that has not been adequately addressed. By seamlessly integrating advances in graph network theory, machine learning, numerical simulations, and high-speed additive manufacturing approaches, this Designing Materials to Revolutionize and Engineer our Future (DMREF) award will accelerate the understanding, inverse design, and fabrication of architected materials with tailorable dynamic fingerprints. The outcome will be materials with inversely designed three-dimensional micro-architectures fabricated via desktop additive manufacturing with prescribed behaviors, such as impact shielding and wave transmission. Applications include energy and shock absorption, acoustic wave filtering, stretchable electronics, and other multifunctional material systems. The project will also train graduate and undergraduate students in the new paradigm of autonomous inverse design and additive manufacturing based on desired behaviors. Moreover, demonstration modules, design games, and additive printing activities will be used for outreach to K-12 students.This project will extend graph-based generative machine learning modeling techniques to identify the underlying motifs within architected materials to understand their dynamic behaviors as well as provide an inverse design framework for optimized functional responses. The first step is to develop a graph space model to represent an arbitrary architected material composed of an arbitrarily complex 3D micro-architecture, by size, scale, hierarchy, lattice topology, and material attributes. The next step involves obtaining high-fidelity experimental data and higher-order simulation data with large amounts of lower-order experimental data to accelerate the training and discovery process. A forward graph-based machine learning model will be trained on the combined data for functional response prediction. Lastly, the graph neural network with reinforcement learning will be used to generate graphs with the desired properties based on the forward predictive model. This extensive and experimentally validated framework will be used to discover fundamental knowledge pertaining to structural and dynamic characteristics, which will then be leveraged to inversely design materials with prescribed dynamic fingerprint.This project is co-funded by the Division of Civil, Mechanical and Manufacturing Innovation (CMMI) in the Directorate for Engineering (ENG) and the Division of Information and Intelligent Systems (IIS) in the Directorate for Computer and Information Science and Engineering (CISE).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.