Data supporting the publication: Fine-Tuning Universal Machine-Learned Interatomic Potentials for Applications in the Science of Steels

This dataset contains the supplemental data for the study "Fine-Tuning Universal Machine-Learned Interatomic Potentials for Applications in the Science of Steels". The primary research objective is to enhance the accuracy of foundational models—specifically CHGNet, MACE or SevenNet to better predict the properties of bcc Fe system. The dataset was generated using VASP and it was subsequently used to fine-tune these potentials. The archive includes the complete fine-tuning outputs, model checkpoint files (`*.pth.tar`), and validation results, offering a comprehensive benchmark for evaluating MLIP performance on iron-based materials.