## 7 Discussion and Conclusions  
Testing the design of an intelligent tool for generating the geometry of a unit cell of a metasurface has confirmed the following conclusions: It is possible to design and simulate metasurfaces for various applications, including the field of energy harvesting. It is possible to apply information on the behavior of metasurfaces in the field of artificial intelligence. It is possible to develop an intelligent tool for generating the desired unit cell geometry of a metasurface for a given resonant frequency. Such a hierarchy and style of generating unit cell geometry can be applied to data close to the actual values of microwave radiation, for which the phenomenon of energy harvesting is possible.  
For further work, a few optimizations may be considered. The multilayer perceptron could be enhanced with optimization algorithms such as genetic algorithms, particle swarm optimization, or reinforcement learning to automate the discovery of the most efficient geometries. This would ensure not only functionality but also performance optimization, such as minimizing loss or enhancing absorption efficiency.  
Implementing generative artificial networks (GANs) could open the door to generating entirely new and previously unexplored metasurface configurations. This approach could be beneficial in discovering innovative geometries that may not be intuitive in traditional design processes.  
In the future, the optimization of the current model and stored database can be made. The model was trained on a limited dataset of 100 geometries. Expanding this dataset to include more variations in unit cell geometries, material properties, and frequency ranges could improve the generalization ability of the neural network. This could be achieved by integrating real-world experimental data with the simulated results to create a more robust dataset.  
The current simulations, while effective, utilize a specific step size to balance computational efficiency and accuracy in determining the resonant frequency. Reducing the step size in future simulations could provide more precise results but at the cost of significantly increased computation times. To mitigate this issue, an alternative approach could involve applying approximation methods for resonant frequency identification. For example, after the initial simulations have provided a rough estimate, finer-resolution simulations could be selectively used in the regions of interest, where approximation techniques could help pinpoint the resonant frequency more efficiently. This strategy would allow for both time  
Figure 11: Adjusted absorption parameters: The simulation result for the absorption and S-11 parameters for the unit cell geometry generated by the smart tool. The binary version of the tested metasurface unit cell is shown on the graph.  
savings and high accuracy without requiring exhaustive high-resolution simulations across the entire frequency range.  
By adopting such methods, future investigations could achieve a better balance between precision and computational efficiency, optimizing both the design process and the performance of the generated metasurface geometries.  
Conceptualization, P.G.; Methodology, P.G.; Software, P.G.; Writing--original draft, P.G.; Writing--review & editing, P.L.; Supervision, P.L.; Funding acquisition, P.L. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Science Centre (NCN, Poland), grant no. 2022/47/I/ST7/02055, and the Deutsche Forschungsgemeinschaft (DFG, Germany), grant no. ME 2093/9-1, within the OPUS-24 (LAP) project \"Investigation of electromagnetic properties of terahertz meta-surfaces tunable using multidirectional magnetic field\".  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author/s.  
The authors declare no conflicts of interest.