Discrepencies and error metrics -- ColabFit
Collection
Datasets from Liu, He & Mo, NPJ 2023 (https://doi.org/10.1038/s41524-023-01123-3) in ColabFit format • 5 items • Updated
property_id string | property_hash string | last_modified timestamp[us] | dataset_id string | multiplicity int32 | software string | method string | energy float64 | atomic_forces list | cauchy_stress list | cauchy_stress_volume_normalized bool | electronic_band_gap float64 | electronic_band_gap_type string | formation_energy float64 | adsorption_energy float64 | atomization_energy float64 | max_force_norm float64 | mean_force_norm float64 | energy_above_hull float64 | configuration_id string | configuration_hash string | structure_hash string | cell list | positions list | pbc list | chemical_formula_hill string | chemical_formula_reduced string | chemical_formula_anonymous string | elements list | elements_ratios list | atomic_numbers list | nsites int32 | nelements int32 | nperiodic_dimensions int32 | dimension_types list | names list | labels list | property_metadata_path string | configuration_metadata_path string | hash string | configuration_metadata string | property_metadata string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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End of preview. Expand in Data Studio
Cite this dataset
Liu, Y., He, X., and Mo, Y. discrepencies and error metrics NPJ 2023 vacancy enhanced training set. ColabFit, 2023. https://doi.org/10.60732/5a780a3a
Cite this dataset
Liu, Y., He, X., and Mo, Y. discrepencies and error metrics NPJ 2023 vacancy enhanced training set. ColabFit, 2023. https://doi.org/10.60732/5a780a3aThis dataset has been curated and formatted for the ColabFit Exchange
This dataset is also available on the ColabFit Exchange:
https://materials.colabfit.org/id/DS_qxd7wv9yabtp_0
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Dataset Name
discrepencies and error metrics NPJ 2023 vacancy enhanced training set
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.
Dataset authors
Yunsheng Liu, Xingfeng He, Yifei Mo
Publication
https://doi.org/10.1038/s41524-023-01123-3
Original data link
https://github.com/mogroupumd/Silicon_MLIP_datasets
License
CC-BY-4.0
Number of unique molecular configurations
218
Number of atoms
13389
Elements included
Si
Properties included
energy, atomic forces
Usage
ds.parquet: Aggregated dataset information.co/directory: Configuration rows each include a structure, calculated properties, and metadata.cs/directory : Configuration sets are subsets of configurations grouped by some common characteristic. Ifcs/does not exist, no configurations sets have been defined for this dataset.cs_co_map/directory : The mapping of configurations to configuration sets (if defined).
ColabFit Exchange documentation includes descriptions of content and example code for parsing parquet files:
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