UCLA

Architected metamaterials are a class of engineered materials with artificially designed structure at micro- or nano-scale that exhibit unusual properties by the interplay between the constitutive materials and the engineered architectures. Recent advancement in additive manufacturing has enabled the integration of functionalities with structural metamaterials via mixing multi-functional particles with 3D-printable materials. However, accurate design of functional performance of as-fabricated metamaterial has not been demonstrated for the following reasons. Attributed to weak interfacial behaviors, physical mixture of functional particles with 3D-printable matrix demonstrates low functional performances, which is several orders of magnitude of homogeneous functional materials. Prior studies has employed surface functionalization to enhance the effective performance of functional composites, while the effect of this process remains elusive. Additionally, despite that the structure properties of metamaterials have been thoroughly studied, it is still unclear how the concept of structural metamaterials can be translated to multi-functional coupling behaviors (i.e., electro-mechanical coupling, thermo-electric coupling, etc.).This work aimed to develop a theoretical design framework which will enable accurate creation of functional performance via guiding the formulation of constitutive material and architectural design of multi-functional metamaterial. In specific, a theoretical model, effective interphase model, is established to characterize the interaction between functional particles and matrix materials, which enables the realization of desired functional properties of composite colloids for additive manufacturing via tuning the formulating parameters like particle loading, surface functionalization level, etc. Next, design of effective performance of functional metamaterial via manipulating its spatial arrangement is investigated and demonstrated. This design strategy is applied in tailoring the anisotropy of piezoelectric material constrained by the intrinsic crystal structure, decoupling the electro-mechanical responses in each orthogonal directions as load orientation and magnitude sensor, and creating all physically feasible actuation modes as actuators with simple electrode arrangements. Additionally, a machine learning based design framework is developed to inverse design the desired compressive response of metamaterials. This machine learning framework breaks the limitation on designing a few mechanical properties of existing methods and enables the re-creation of full temporal and spatial mechanical response of metamaterials. In general, this work provides a comprehensive design methodology of functional behaviors which characterizes the effect of both the constitutive material properties and architectures of multi-functional metamaterials.