DMREF/Collaborative Research: Design and Optimization of Granular Metamaterials using Artificial Evolution

能够获得多种属性的超材料将导致系统拥有广泛的功能。这种多功能超材料将通过动态适应任务需求或环境变化来提高系统和结构的自主性、效率和寿命。颗粒状超材料是这种动态可编程性的有利平台，因为可以广泛地调整颗粒特性以实现不同的响应。颗粒超材料响应取决于许多变量，包括颗粒布置、颗粒质量、模量和形状、颗粒之间的摩擦或其他相互作用，以及容器的壁是否保持固定或可以响应于来自颗粒的力而移动。由于微观结构变量的可能组合如此之多，设计微观结构和整体性质之间的复杂关系的任务是艰巨的。这项设计材料以革命和工程我们的未来（DMREF）研究应用进化算法来有效地搜索特定材料特性的颗粒超材料设计的巨大参数空间，以及确定如何扰动设计，以积极地从一组所需的整体特性过渡到另一组。该项目将建立一种新的人工智能驱动的方法来设计和优化具有适应性的颗粒超材料。这些材料将通过提供额外的手段来寻找和使用影响机器人和其他技术领域的材料，从而帮助美国的生产力和繁荣。未来的颗粒超材料将表现出更高的动态可塑性，通过重新配置其物理结构，使其能够响应不同的环境输入或任务需求。本计画主要解决两个问题：（1）如何自动化设计具有特定材料特性的颗粒集合体？(i.e.,颗粒的排列、模量、形状、质量、摩擦系数和其他颗粒尺度的性质应该是什么，以产生给定的散装材料性质？以及（2）如何自动设计一系列允许材料性质连续、按时间顺序变化的颗粒集合体？(i.e.,哪一组粒度变量应该改变，改变多少？如果有多个解决方案，哪一个在实验中最容易实现？）为了解决这些问题，将开发和实施一个闭环过程，其中基于物理的离散元方法（DEM）模拟，基于进化的优化和物理实现相结合，以产生具有所需，最佳和适应性材料特性的颗粒超材料。该项目将产生的新知识和工具包括：（一）设计能够改变其自身结构和性质的粒状超材料的替代进化算法;（二）基于物理学的离散元模拟，可预测具有活性颗粒的粒状材料的性质;以及（iii）多功能颗粒和颗粒组件的新合成策略。该奖项反映了NSF的法定使命，并被认为值得支持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。

Metamaterials capable of accessing multiple properties will lead to systems possessing a wide range of functions. Such multifunctional metamaterials will increase the autonomy, efficiency, and lifespan of systems and structures by dynamically adapting to task demands or changes in the environment. Granular metamaterials are an advantageous platform for such dynamic programmability, as the grain properties can be widely tuned to achieve different responses. Granular metamaterial response is dependent on many variables, including the grain arrangement, grain mass, modulus, and shape, friction or other interactions between grains, as well as whether the walls of the container are held fixed or can move in response to forces from the grains. With such a vast number of possible combinations of micro-structural variables, the task of designing the complex relationship between micro-structure and bulk properties is daunting. This Designing Materials to Revolutionize and Engineer our Future (DMREF) research applies evolutionary algorithms to efficiently search the immense parameter space of granular metamaterial designs for specific material properties, as well as identifying how a design can be perturbed to actively transition from one set of desired bulk properties to another. The project will establish a new artificial intelligence-driven approach to the design and optimization of granular metamaterials with adaptable properties. These materials will aid US productivity and prosperity by providing additional means to find and use materials impacting robotics and other technical areas.Future granular metamaterials will exhibit increased dynamic plasticity, enabling responses to different environmental inputs or task demands by reconfiguring their physical structure. This project addresses two issues: (1) How can granular assemblies with specific desired material properties be automatically designed? (i.e., What should the grains' arrangement, moduli, shapes, masses, friction coefficients, and other grain-scale properties be to yield a given bulk material property?); and (2) How can a series of granular assemblies that allow continuous, time-ordered changes in material properties be automatically designed? (i.e., Which set of grain-scale variables should change and by how much? If multiple solutions are found, which ones can be realized in experiments most easily?) To address these issues, a closed-loop procedure will be developed and implemented wherein physics-based discrete element method (DEM) simulations, evolution-based optimization, and physical realizations are combined to produce granular metamaterials with desired, optimal, and adaptable material properties. New knowledge and tools that will be generated by this project include: (i) alternative evolutionary algorithms for designing granular metamaterials capable of changing their own configuration and properties; (ii) physics-based DEM simulations that can predict the properties of granular materials with active grains; and (iii) New synthesis strategies for multifunctional grains and grain assemblies.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.