The total training time was 25 minutes, with 11s for each epoch. Upon completion of the training phase, we evaluated the performance of our model using standard evaluation metrics. The model achieved a mean absolute error (MAE) value of a 3.87 and a mean squared error (MSE) value of 83.67. It is worth mentioning that the Mat2Spec model was trained and executed using various combinations of data scaling and training loss functions, effectively creating an ensemble of separately optimized models. Among these models, the most successful one, based on all measures except for MAE, utilized SumNorm scaling and KL loss, achieving a MAE of 3.8 and MSE of 74.5. Figure shows the predicted eDOS spectra generated by our model. Although the results exhibit a reasonable approximation of the general behavior of eDOS, it is important to note that our model is currently unable to faithfully reproduce the narrow peaks observed in the reference dataset, nor can the Mat2Spec model. The current model shows promising predictive capabilities, there is potential for further refinement to address the challenges associated with accurately capturing the fine details present in the eDOS patterns. By incorporating this feedback and building upon our current methodology, we aim to enhance the predictive performance of our model for other electronic quantities of MP dataset.