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

材料的力-位移响应、模态响应以及对动态载荷的波传输和吸收响应都可以被解释为其特征指纹。由于材料微观结构、几何形状和施加的载荷之间复杂的非线性相互作用，在几分之一秒内施加的动态载荷下材料的行为仍然知之甚少。复杂性增加了多方面的架构材料，其中拓扑考虑是至关重要的，以实现特定的响应或功能。因此，具有最佳动态指纹的结构材料的有条理的设计是一个尚未得到充分解决的挑战。通过无缝集成图形网络理论，机器学习，数值模拟和高速增材制造方法的进步，这个设计材料革命和工程师我们的未来（DMREF）奖将加速理解，逆向设计和制造具有可定制动态指纹的建筑材料。其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）的逆向设计三维微结构的材料。应用包括能量和减震，声波过滤，可拉伸电子产品和其他多功能材料系统。该项目还将培训研究生和本科生在自主逆向设计和基于期望行为的增材制造的新范式。此外，示范模块，设计游戏和增材打印活动将用于K-12学生的推广。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图案，以了解其动态行为，并提供优化功能响应的逆向设计框架。第一步是开发一个图形空间模型来表示由任意复杂的3D微结构组成的任意建筑材料，通过大小，尺度，层次结构，晶格拓扑和材料属性。下一步涉及获得高保真实验数据和高阶仿真数据以及大量低阶实验数据，以加速训练和发现过程。基于图的前向机器学习模型将在组合数据上进行训练，用于功能反应预测。最后，将使用具有强化学习的图神经网络来基于前向预测模型生成具有所需属性的图。这个广泛的和实验验证的框架将用于发现与结构和动态特性有关的基本知识，然后将利用这些知识来逆向设计具有规定动态指纹的材料。工程局（ENG）和信息和智能系统部（IIS）的机械和制造创新（CMMI）该奖项反映了NSF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.