Computational topology optimization provides a systematic, mathematically driven framework for navigating this new design challenge. Traditional implementations of structural optimization occur at the macroscale level, i.e., a selected material is distributed over a design domain such that an objective function, e.g., compliance or structural mass, is minimized while satisfying a set of constraints. To achieve the optimal material distribution, the homogenization method [
7] or the SIMP interpolation model [
8,
9] are commonly used. Topology optimization is not restricted to the optimal distribution of a single material, but several materials can be considered [
10,
11]. Topology optimization has also been employed to design novel materials with extreme properties [
12,
13] such as negative Poisson’s ratio [
14,
15], thermal expansion coefficient [
16], fluid permeability [
17,
18], piezoelectric properties [
19] and phononic properties [
20,
21], to name a few. To tailor the effective properties of the designed material, the inverse homogenization method [
22] has been widely employed. Alternatively, in cases where homogenization may not apply, such as optimizing energy absorption considering material plasticity [
23,
24], one can create a finitely periodic representation of the material at significantly increased computational cost. When designing novel materials, the optimization process is usually carried out with a general idea of future potential needs rather than focusing on a specific application. Ergo, when applying the optimized material to a specific application or an existing structural design, the resulting design may not be optimal due to the uncoupled characteristic of the process. The idea of designing simultaneously the structure and the material is thus appealing since it would lead to a structure exhibiting an optimized macrostructural layout with architectured materials that account for the structure boundary conditions.