The baseline performance of the pretrained MMT model without the improvement cycle, detailed in Supplementary Information Section 6, demonstrates the model's initial limitations: solving only 58% of our simulated data, 16% of ACD Labs simulated data, and a mere 3% of real experimental data. In contrast, the improved model achieves high accuracy on experimental data, highlighting the effectiveness of our improvement cycle. This dramatic enhancement, despite training solely on simulated spectra, underscores that comprehensive chemical space coverage is more critical than precise training data accuracy. The model's ability to adapt to various spectral simulation methods and real experimental data showcases its robustness and generalizability, crucial for diverse research environments. These results demonstrate the model's practical applicability in real-world structure elucidation tasks, with the potential to significantly accelerate and improve the structure determination process in chemistry laboratories. Furthermore, the model's adaptability to discrepancies between simulated and experimental data suggests that additional fine-tuning with real experimental data could yield even greater improvements, opening avenues for further enhancing its performance in practical settings.