Triply periodic minimal surfaces (TPMS) are a class of metamaterials with a variety of applications and well-known primitives. We present a new method for discovering novel microscale TPMS structures with exceptional energy-dissipation capabilities, achieving double the energy absorption of the best existing TPMS primitive structure. Our approach employs a parametric representation, allowing seamless interpolation between structures and representing a rich TPMS design space. We show that simulations are intractable for optimizing microscale hyperelastic structures, and instead propose a sample-efficient computational strategy for rapidly discovering structures with extreme energy dissipation using limited amounts of empirical data from 3D-printed and tested microscale metamaterials. This strategy ensures high-fidelity results but involves time-consuming 3D printing and testing. To address this, we leverage an uncertainty-aware Deep Ensembles model to predict microstructure behaviors and identify which structures to 3D-print and test next. We iteratively refine our model through batch Bayesian optimization, selecting structures for fabrication that maximize exploration of the performance space and exploitation of our energy-dissipation objective. Using our method, we produce the first open-source dataset of hyperelastic microscale TPMS structures, including a set of novel structures that demonstrate extreme energy dissipation capabilities. We show several potential applications of these structures in protective equipment and bone implants.

翻译：三重周期极小曲面（TPMS）是一类具有广泛应用且包含多种已知基本结构的超材料。我们提出一种新方法，用于发现具有卓越能量耗散能力的新型微尺度TPMS结构，其能量吸收性能达到现有最佳TPMS基本结构的两倍。我们的方法采用参数化表征，可实现结构间的无缝插值，从而表征一个丰富的TPMS设计空间。我们证明，通过仿真优化微尺度超弹性结构在计算上不可行，因此提出一种样本高效的计算策略，利用从3D打印和测试的微尺度超材料中获得的有限经验数据，快速发现具有极端能量耗散特性的结构。该策略虽能保证高保真度结果，但涉及耗时的3D打印与测试流程。为解决此问题，我们采用不确定性感知的深度集成模型来预测微观结构行为，并确定下一步需要3D打印和测试的结构。通过批量贝叶斯优化，我们迭代优化模型，选择那些能最大限度探索性能空间并充分利用我们能量耗散目标的候选结构进行制造。运用本方法，我们创建了首个开源的超弹性微尺度TPMS结构数据集，其中包含一系列展现极端能量耗散能力的新型结构。我们展示了这些结构在防护装备和骨植入物领域的若干潜在应用。