Furthermore, we introduce an innovative improvement cycle that allows the model to adapt to unseen chemical spaces. This iterative process enhances the model's ability to predict structures in novel domains, effectively expanding its applicability. Importantly, this improvement cycle enables our model to solve real experimental spectra despite being trained initially on simulated spectra. This capability demonstrates the model's robustness and potential for practical applications in real-world structure elucidation tasks.