Mechanical metamaterials are architected materials whose mechanical properties are determined by the combination of a bulk material and a designed microstructure. Therefore, these materials are ideally suited for inverse design tasks, i.e., for finding a specific metamaterial for a given property [2]. Within this contribution, we present a neural network (NN)-based inverse design approach and apply it to spinodoid architected materials [1]. In recent years, spinodoid metamaterials inspired by naturally occurring spinodal topologies gained a lot of attention due to some favourable properties like their tunable anisotropy and non-periodicity, and thus robustness against symmetry-breaking defects [1]. These structures offer a vast design space defined by only a small set of design parameters, i.e., descriptors. The relatively simple parametric representation of those complex spinodoid topologies poses a unique opportunity to create architected materials with precisely adjustable mechanical properties, making them favourable for the application in inverse design. After introducing the design possibilities of the spinodoid architectures, they are further explored with regard to achievable elastic properties based on the formulation of an inverse design task. Specifically, we solve two different design tasks: maximizing stiffness in one direction for a given density and mimicking a fully specified stiffness. References [1] Kumar, S., Tan, S., Zheng, L. & Kochmann, D. M., Inverse-designed spinodoid metamaterials, npj Comput. Mater. 6, 73 (2020). [2] Raßloff, A., Seibert, P., Kalina, K. A., & Kästner, M., Inverse design of spinodoid structures using Bayesian optimization, Computational Mechanics, (2025).