Metamaterials are artificially structured materials with
unusual
properties, such as negative Poisson’s ratio, acoustic band
gap, and energy absorption. However, metamaterials made of conventional
materials lack tunability after fabrication. Thus, active metamaterials
using magneto-mechanical actuation for untethered, fast, and reversible
shape configurations are developed to tune the mechanical response
and property of metamaterials. Although the magneto-mechanical metamaterials
have shown promising capabilities in tunable mechanical stiffness,
acoustic band gaps, and electromagnetic behaviors, the existing demonstrations
rely on the forward design methods based on experience or simulations,
by which the metamaterial properties are revealed only after the design.
Considering the massive design space due to the material and structural
programmability, a robust inverse design strategy is desired to create
the magneto-mechanical metamaterials with preferred tunable properties.
In this work, we develop an inverse design framework where a deep
residual network replaces the conventional finite-element analysis
for acceleration, realizing metamaterials with predetermined global
strains under magnetic actuations. For validation, a direct-ink-writing
printing method of the magnetic soft materials is adopted to fabricate
the designed complex metamaterials. The deep learning-accelerated
design framework opens avenues for the designs of magneto-mechanical
metamaterials and other active metamaterials with target mechanical,
acoustic, thermal, and electromagnetic properties.