  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.
