Manipulating the architecture of materials to achieve optimal combinations of properties (inverse design) has always been the dream of materials scientists and engineers. Lattices represent an efficient way to obtain lightweight yet strong materials, providing a high degree of tailorability. Despite massive research has been done on lattice architectures, the inverse design problem of complex phenomena (such as structural instability) has remained elusive. Via deep neural network and genetic algorithm, we provide a machine-learning-based approach to inverse-design non-uniformly assembled lattices. Combining basic building blocks, our approach allows us to independently control the geometry and topology of periodic and aperiodic structures. As an example, we inverse-design lattice architectures with superior buckling performance, outperforming traditional reinforced grid-like and bio-inspired lattices by ~30–90% and 10–30%, respectively. Our results provide insights into the buckling behavior of beam-based lattices, opening an avenue for possible applications in modern structures and infrastructures.

操纵材料的结构以实现性能的最佳组合（逆向设计）一直是材料科学家和工程师的梦想。晶格代表了一种获得轻质但坚固材料的有效方法，提供了高度的可裁剪性。尽管对晶格结构进行了大量研究，但复杂现象（如结构不稳定性）的逆向设计问题仍然难以捉摸。通过深度神经网络和遗传算法，我们提供了一种基于机器学习的方法来逆向设计非均匀组装的格子。结合基本构建块，我们的方法使我们能够独立控制周期性和非周期性结构的几何形状和拓扑结构。例如，我们逆向设计具有卓越屈曲性能的晶格架构，分别优于传统的增强型网格和仿生晶格约 30-90% 和 10-30%。我们的结果提供了对基于梁的晶格的屈曲行为的见解，为现代结构和基础设施中的可能应用开辟了道路。