Designing architected metastructures with desirable characteristics is typically associated with complex fabrication and testing procedures. The challenges ahead for nanoscale fabrication of these engineered structures lead to severe obstacles to investigate their complex design patterns and corresponding mechanical properties. Here, we introduce a striking artificial intelligence concept based on evolutionary computation for the characterization of the mechanical properties of nanoscale metastructures with corrugation. The corrugated metastructures are fabricated and experimentally tested at the nanoscale. New evolutionary computation-based models are presented to characterize and predict the tensile stiffness and postbuckling compressive stiffness of the metastructures. The proposed models are the outcomes of an extensive simulation process which involved evolving millions of design models with different combination of the predictor variables. Thousands of data obtained from the experimentally-calibrated numerical models are used to develop the models. Further experiments are carried out to verify the efficiency of the evolutionary computation models for the prediction of the tensile and postbuckling compressive stiffnesses. In the end, we envision that the proposed evolutionary computation approach can be integrated with topology optimization to evolve optimized metastructures with programmable mechanical characteristics.

设计具有所需特性的架构化元结构通常与复杂的制造和测试过程相关。这些工程结构的纳米级制造面临的挑战导致严重的障碍，难以研究其复杂的设计模式和相应的机械性能。在这里，我们介绍了一种基于进化计算的引人注目的人工智能概念，用于表征具有波纹的纳米级元结构的机械性能。波纹状的微结构是在纳米级制造和实验测试的。提出了基于演化计算的新模型，以表征和预测元结构的拉伸刚度和屈曲后的压缩刚度。所提出的模型是广泛仿真过程的结果，该过程涉及使用预测变量的不同组合来发展数百万个设计模型。从实验校准的数值模型获得的数千个数据用于开发模型。进行了进一步的实验，以验证用于预测抗拉刚度和屈曲后抗压刚度的演化计算模型的效率。最后，我们设想可以将所提出的演化计算方法与拓扑优化集成在一起，以演化具有可编程机械特性的优化元结构。从实验校准的数值模型获得的数千个数据用于开发模型。进行了进一步的实验，以验证用于预测抗拉刚度和屈曲后抗压刚度的演化计算模型的效率。最后，我们设想可以将所提出的演化计算方法与拓扑优化集成在一起，以演化具有可编程机械特性的优化元结构。从实验校准的数值模型获得的数千个数据用于开发模型。进行了进一步的实验，以验证用于预测拉伸和屈曲后压缩刚度的演化计算模型的效率。最后，我们设想可以将所提出的演化计算方法与拓扑优化集成在一起，以演化具有可编程机械特性的优化元结构。