Dataset

discrepencies_and_error_metrics_NPJ_2023_interstitial_enhanced_training_set


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Name :
discrepencies_and_error_metrics_NPJ_2023_interstitial_enhanced_training_set
Extended ID :
discrepencies_and_error_metrics_NPJ_2023_interstitial_enhanced_training_set__Liu-He-Mo__DS_nublbp38wse0_0
ColabFit ID :
DS_nublbp38wse0_0
Files :
colabfitspec.json
Description :
Structures from discrepencies_and_error_metrics_NPJ_2023 training set, enhanced by inclusion of interstitials. 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/f0a44294 https://commons.datacite.org/doi.org/10.60732/f0a44294 https://doi.datacite.org/dois/10.60732%2Ff0a44294 https://doi.org/10.60732/f0a44294 Cite as: Liu, Y., He, X., and Mo, Y. "discrepencies and error metrics NPJ 2023 interstitial enhanced training set." ColabFit, 2023. https://doi.org/10.60732/f0a44294.
For other citation formats, see the DataCite Fabrica page for this dataset.
Num. Configurations :
218
Num. Atoms :
13,629
Downloads :
28
Calculated Property Types :
atomic_forces cauchy_stress energy
Elements :
Si (100.0%)
Methods :
DFT-PBE
Software :
VASP 5.4.4
Publication Link :
https://doi.org/10.1038/s41524-023-01123-3
Data Source Link :
https://github.com/mogroupumd/Silicon_MLIP_datasets
Configuration Sets by Name :
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