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 properties, including exceptionally stiff, auxetic, and pentamode-like designs. 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——其中桁架点阵是最具代表性的一类。尽管其可定制性能已被广泛探索，但由于设计空间离散且庞大，加之缺乏全面的参数化方法，基于桁架的超材料设计仍然高度受限且常依赖经验。我们在此提出一种基于图的深度学习生成框架，该框架结合了变分自编码器与性能预测器，构建了一个覆盖海量桁架结构的精简连续潜空间。这一统一的潜空间允许通过简单操作（如遍历潜空间或在结构间插值）快速生成新设计。我们进一步展示了一个优化框架，用于逆向设计具有定制化性能的桁架，包括超高刚度、拉胀性和类五模超材料设计。该生成模型能够预测训练域之外具有极端目标性能（且反直觉）的可制造设计。