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Abstract The implementation of optimized structural design recommender systems utilizing Artificial Intelligence (AI) offers unprecedented opportunities for enhancing the economic viability of vessels. By representing complex ship structural design data as a graph, one can leverage the unique characteristics of Graph Neural Networks (GNNs) to propose design solutions to naval architects during the basic design process. This approach enables the generation of designs in a reduced time frame while facilitating the immediate identification and investigation of potential errors, thereby impacting the ship's cost, performance, weight, stability, safety, and manufacturability. INTRODUCTION The discovery and design of new structural elements design with specific properties are important for advancing technologies in the maritime industry. The basic design stage is a relatively fast process but an essential part of the ship's lifecycle. It is not straightforward to measure the downstream impact of the quality of basic design. Errors in the detailed engineering stage, costly changes in production, lack of efficiency, predictability, and profitability in operations are all aspects that are not easy to consider when an experienced naval architect defines the configuration of a new ship. These aspects have an immediate impact on the ship cost, performance, weight, stability, safety, and manufacturability. (Florean, Alonso de los Rios, Pietro, & Seppälä, 2022) Traditional approaches to design discovery are time-consuming and expensive, limiting the pace of innovation. Analysis methods, such as CFD calculations, based on optimization algorithms, and the recent advancements in universal data exchange standards like OCX, have significantly accelerated this process. However, even these methods can be constrained by the complexity of the data, the number of stakeholders, and the processes involved. Moreover, traditional approaches draw a clear line between design and engineering, where early design models focus on the vessel's fundamental characteristics - naval architectural and functional aspects – while detailed design and production design deal with the layout intricacies and the generation of production and construction data. (Koelman, Veelo, Seppälä, & Filius, 2024) This separation often results in missing design considerations and leads to potential errors that could have been prevented in a more related system.