Our initial experiments focused on optimizing the MMT model, which processes various spectral data types including NMR ( 1 H, C, HSQC, and COSY), IR, and MS. We evaluated different model configurations and training strategies using metrics such as SMILES prediction accuracy, structural similarity, and SMILES validity of generated molecules. Larger models and datasets consistently improved performance across all metrics, leading us to select the largest model configuration and a 4 million molecule dataset for further analysis. We implemented a three-stage training strategy, progressively incorporating SMILES prediction, molecular weight loss, and spectral data dropout, which enhanced the model's overall performance. For more detailed information on the optimization process and experimental results, please refer to Supplementary Information Section 2. we evaluated the model's molecule identification accuracy using HSQC spectral matching, achieving an 89.9% accuracy with multinomial sampling, significantly outperforming greedy sampling which achieved 50.0% accuracy. These optimizations establish a robust foundation for the MMT model's application in molecular structure elucidation from spectral data. More detailed information on this experiment can be found in Supplementary Information Section 3.