Name: discrepencies_and_error_metrics_NPJ_2023_vacancy_enhanced_training_set Extended ID: discrepencies_and_error_metrics_NPJ_2023_vacancy_enhanced_training_set__Liu-He-Mo__DS_qxd7wv9yabtp_0 Description: Structures from discrepencies_and_error_metrics_NPJ_2023 training set; includes some structures with vacancies. The full discrepencies_and_error_metrics_NPJ_2023 dataset includes the original mlearn_Si_train dataset, modified with the purpose of developing models with better diffusivity scores by replacing ~54% of the data with structures containing migrating interstitials. The enhanced validation set contains 50 total structures, consisting of 20 structures randomly selected from the 120 replaced structures of the original training dataset, 11 snapshots with vacancy rare events (RE) from AIMD simulations, and 19 snapshots with interstitial RE from AIMD simulations. We also construct interstitial-RE and vacancy-RE testing sets, each consisting of 100 snapshots of atomic configurations with a single migrating vacancy or interstitial, respectively, from AIMD simulations at 1230 K. Authors: Yunsheng Liu Xingfeng He Yifei Mo DOI: 10.60732/5a780a3a Calculated Property Types: atomic_forces cauchy_stress energy Elements: Si (100.0%) Methods: DFT-PBE Software: VASP 5.4.4 Number of Configurations: 218 Number of Atoms: 13,389 Publication Link: https://doi.org/10.1038/s41524-023-01123-3 Data Source Link: https://github.com/mogroupumd/Silicon_MLIP_datasets