Architected materials possessing physico-chemical properties adaptable to disparate environmental conditions embody a disruptive new domain of materials science. Fueled by advances in digital design and fabrication, materials shaped into lattice topologies enable a degree of property customization not afforded to bulk materials. A promising venue for inspiration toward their design is in the irregular micro-architectures of nature. However, the immense design variability unlocked by such irregularity is challenging to probe analytically. Here, we propose a new computational approach using graph-based representation for regular and irregular lattice materials. Our method uses differentiable message passing algorithms to calculate mechanical properties, therefore allowing automatic differentiation with surrogate derivatives to adjust both geometric structure and local attributes of individual lattice elements to achieve inversely designed materials with desired properties. We further introduce a graph neural network surrogate model for structural analysis at scale. The methodology is generalizable to any system representable as heterogeneous graphs.

翻译：具有可适应不同环境条件的物理化学性质的人造材料，代表了材料科学中一个突破性的新领域。在数字化设计与制造技术进步的推动下，成形为晶格拓扑结构的材料实现了块体材料无法提供的性能定制化程度。自然界中不规则的微结构为其设计提供了富有前景的灵感来源。然而，这种不规则性所释放的巨大设计变异性使得解析性探索面临挑战。本文提出了一种基于图表示的新型计算方法，适用于规则与不规则晶格材料。该方法利用可微消息传递算法计算力学性能，从而通过替代导数实现自动微分，以调整单个晶格单元的几何结构和局部属性，进而逆向设计出具有目标性能的材料。我们进一步引入图神经网络替代模型进行大规模结构分析。该方法论可推广至任何可用异构图表示的系统。