The rise of machine learning has fueled the discovery of new materials and, especially, metamaterials--truss lattices being their most prominent class. While their tailorable properties have been explored extensively, the design of truss-based metamaterials has remained highly limited and often heuristic, due to the vast, discrete design space and the lack of a comprehensive parameterization. We here present a graph-based deep learning generative framework, which combines a variational autoencoder and a property predictor, to construct a reduced, continuous latent representation covering an enormous range of trusses. This unified latent space allows for the fast generation of new designs through simple operations (e.g., traversing the latent space or interpolating between structures). We further demonstrate an optimization framework for the inverse design of trusses with customized mechanical properties in both the linear and nonlinear regimes, including designs exhibiting exceptionally stiff, auxetic, pentamode-like, and tailored nonlinear behaviors. This generative model can predict manufacturable (and counter-intuitive) designs with extreme target properties beyond the training domain.

翻译：机器学习的兴起推动了新材料的发现，尤其是超材料——其中桁架格构是最突出的类别。尽管其可定制属性已被广泛探索，但由于庞大离散的设计空间以及缺乏全面的参数化方法，基于桁架的超材料设计始终高度受限且常依赖启发式方法。我们在此提出一种基于图的深度学习生成框架，该框架结合变分自编码器与属性预测器，构建覆盖海量桁架范围的简化连续隐空间表征。这一统一隐空间可通过简单操作（如遍历隐空间或在结构间插值）快速生成新设计。我们进一步展示了在线性和非线性两种工况下针对定制化力学性能的桁架逆设计优化框架，包括具有超刚度、拉胀、类五模及定制非线性行为的结构。该生成模型能够预测训练域外具有极端目标性能的可制造（甚至反直觉）设计。