where The training dataset composed of the various data frames was randomly divided into three datasets for training (80%), validation (10%), and testing (10%). The purpose of this division was to avoid data leakage when the constructed model was tested after NNP construction. The predicted energy, force, and virial tensor from the constructed NNP were compared with DFT-MD-derived test data. All datasets, including glass structures and the developed fine-tuned model, are available from the Zenodo repository. A direct comparison of the two NNP models (fine-tuned and non-fine-tuned) was also performed to determine the effects of fine-tuning. A commonly used NNMD procedure to simulate oxide glasses is shown in Fig. . The initial structures were prepared using CMD-Du, using the same procedure as for DFT-MD, as described in Fig. . To avoid non-physical interactions, a suitable initial structure derived from CMD-Du was used rather than a random structure.