Piezoelectric truss metamaterials: data-driven design and additive manufacturing

Abstract

The inherent directionality of piezoelectric materials is constrained by the symmetry of their crystal structure, which limits the property space in natural piezoelectric materials. To alleviate this limitation, one could leverage geometry or architecture at the mesoscale. Here, we present a framework for designing and 3D-printing piezoelectric truss metamaterials with customizable anisotropic responses. We employ generative machine learning to design truss metamaterials and achieve unconventional behaviors, including auxetic, unidirectional, and omnidirectional piezoelectricity. Then, we develop an in-gel-3D printing method to fabricate these structures using a composite slurry of photo-curable resin and lead-free piezoelectric particles. We achieve an improvement of over 48% in the specific hydrostatic piezoelectric coefficient in optimized metamaterials over bulk lead zirconate titanate (PZT), and the rare phenomenon of higher transverse piezoelectric coefficients than the longitudinal coefficient. Our approach enables customizable piezoelectric responses and paves the way towards the development of a new generation of electro-active animate materials.