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Abstract Recent advancements in manufacturing, finite element analysis (FEA), and optimization techniques have expanded the design possibilities for metamaterials, including isotropic and auxetic structures, known for applications like energy absorption due to their unique deformation mechanism and consistent behavior under varying loads. However, achieving simultaneous control of multiple properties, such as optimal isotropic and auxetic characteristics, remains challenging. This paper introduces a systematic design approach that combines modeling, FEA, genetic algorithm, and optimization to create tailored mechanical behavior in metamaterials. Through strategically arranging 8 distinct neither isotropic nor auxetic unit cell states, the stiffness tensor in a 5 × 5 × 5 cubic symmetric lattice structure is controlled. Employing the NSGA-II genetic algorithm and automated modeling, we yield metamaterial lattice structures possessing both desired isotropic and auxetic properties. Multiphoton lithography fabrication and experimental characterization of the optimized metamaterial highlights a practical real-world use and confirms the close correlation between theoretical and experimental data. 

Since Multiphoton Lithography (MPL) is applied as an additive manufacturing technique for the fabrication of operational microsystems, the need to predict the mechanical response of the fabricated structures emerges. This work focuses on determining the Young's Modulus of structures fabricated via MPL. With this objective in mind, two series of experiments were designed and conducted: the first one for the determination of the factors whose impact is significant, and the second one for generating a dataset used in the training of a machine learning tool that will define the suitable set of fabrication parameters for the fabrication of a structure with desired Young's Modulus.