chemical_formula_hill
string | chemical_formula_reduced
string | chemical_formula_anonymous
string | atomic_numbers
list | elements
list | elements_ratios
list | nelements
int32 | nsites
int32 | cell
list | positions
list | pbc
list | dimension_types
list | nperiodic_dimensions
int32 | structure_hash
string | multiplicity
int32 | software
string | method
string | adsorption_energy
float64 | atomic_forces
list | atomization_energy
float64 | cauchy_stress
list | cauchy_stress_volume_normalized
bool | electronic_band_gap
float64 | electronic_band_gap_type
string | energy
float64 | formation_energy
float64 | max_force_norm
float64 | mean_force_norm
float64 | property_object_metadata
string | property_object_metadata_id
string | property_object_last_modified
timestamp[ns] | property_object_hash
string | property_object_id
string | configuration_metadata
string | configuration_metadata_id
string | configuration_labels
list | configuration_names
list | configuration_dataset_ids
list | configuration_last_modified
timestamp[ns] | configuration_hash
string | configuration_id
string | dataset_name
string | dataset_authors
list | dataset_description
string | dataset_elements
list | dataset_nelements
int32 | dataset_nproperty_objects
int64 | dataset_nconfigurations
int32 | dataset_nsites
int64 | dataset_adsorption_energy_count
int64 | dataset_atomic_forces_count
int64 | dataset_atomization_energy_count
int64 | dataset_cauchy_stress_count
int64 | dataset_electronic_band_gap_count
int64 | dataset_energy_count
int64 | dataset_energy_mean
float64 | dataset_energy_variance
float64 | dataset_formation_energy_count
int64 | dataset_last_modified
timestamp[ns] | dataset_dimension_types
list | dataset_nperiodic_dimensions
list | dataset_publication_year
string | dataset_total_elements_ratios
list | dataset_license
string | dataset_links
string | dataset_doi
string | dataset_hash
string | dataset_id
string | dataset_extended_id
string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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| [
"Ge",
"Te"
]
| [
0.5,
0.5
]
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[
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"main_gst_gap_22_pbesol_cryst_spc_rs_anti",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs_anti",
"refitted_gst_gap_22_pbe_cryst_spc_rs_anti"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 5287795552173450691578802396637117748088359378339147878707128999580220996208914283651189475203433027028397330582516873018769649289850549108660151908983057 | CO_5287795552173450691578802 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:08 | 5297563260442446322462269641830826476411473048158766787769238392889633819781452833065570374720416532160269252864155020356708853300026298582661110868387781 | CO_5297563260442446322462269 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| [
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]
| 2023-12-05T20:21:07 | 5466764036738110116226085405972203682695626195389531286233027342223604670219404157972696301176971212586714473624940687669388485092033491671946678931591682 | CO_5466764036738110116226085 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -4,766.543734 | null | 3.28425 | 3.28425 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:29:40 | 8361325719898567007263428286142995964490130441639677979963849462103575150848499823769500124739412572728731101489857925992581233382125223268652515082415924 | PO_8361325719898567007263428 | {"config-type": "dimer", "masses": [121.76, 121.76], "sub-config": "dimer", "hash": "12749784377554201583291612343634491581939460251612151514457680212018420488370028909551115401499445089420899938851942171239271722616911573405893787570700809", "id": "MD_1274978437755420158329161"} | MD_1274978437755420158329161 | null | [
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"DS_efcq7a0h6z5w_0",
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"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:03 | 5487543908201579817900083467376380080475546248424504990015771408917121093864082252610338847025480090538022454825211032535243881747968582249120804681483824 | CO_5487543908201579817900083 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| [
3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_2_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_2_standard_iter",
"refitted_gst_gap_22_pbe_iter_2_standard_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 551258303896072675703634442482259908605522381880141170265990615674322727462702160479588937848713763394621190030571628973141317163091773094587835482443339 | CO_5512583038960726757036344 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| [
3
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| 2023 | [
0.23625529319304173,
0.21859567629705223,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 5523335757128352116742977235223762812915135993867623754296139310604245525706560135363844347672678696378841078159810698896117192144047803480095091557592127 | CO_5523335757128352116742977 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_cryst_spc_rs_anti",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs_anti",
"refitted_gst_gap_22_pbe_cryst_spc_rs_anti"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:06 | 5596192359785301573799679588502681259613322069407245431735694698484792644380011433283549967547546304842292957192347852715266481530849676867371462055131532 | CO_5596192359785301573799679 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_3_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_3_standard_iter",
"refitted_gst_gap_22_pbe_iter_3_standard_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 5642636238257494190581365383793311657205675352656294737086544545032211705724277377069807604879764938084819114694957988389979384489557464748928744515837299 | CO_5642636238257494190581365 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_3_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_3_standard_iter",
"refitted_gst_gap_22_pbe_iter_3_standard_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 5653648111856779596216080561325597747202216175676538959343989668576068903079749881221492457225361921611138851389498148507252229990015467555344250316993034 | CO_5653648111856779596216080 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.23625529319304173,
0.21859567629705223,
0.545149030509906
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| [
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| 2023-12-05T20:21:06 | 5656379396278679782633792765860769356225955912530522865651423502981387449004089887324052159010714926365805813406363903986471521268171844151370572572734431 | CO_5656379396278679782633792 | GST_GAP_22_refitted | [
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"main_gst_gap_22_pbesol_cryst_MP",
"extended_gst_gap_22_for_efield_pbesol_cryst_MP",
"refitted_gst_gap_22_pbe_cryst_MP"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:03 | 5757856811721956702089266455725580283467692691479023277786856816064933627235925789677405765810643209502192323997192672246784194311958964348995130164992402 | CO_5757856811721956702089266 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:08 | 5793929463905732046235658486341539032631137701971779656214858806820523548796309785829738164192819652629355689038335987043957428382458015902750591584802719 | CO_5793929463905732046235658 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 5845621622118686468766363829422213220299533813879614342062198924307610608870767879507507429028005411211688658337739755173531056395793062595765941755486863 | CO_5845621622118686468766363 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| true | null | null | -335,935.621313 | null | 0.561344 | 1.960882 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:20:48 | 3557833493337258949730663872864148160432111664006945170081878958486923776473250360023034709067606746458503903613380312350500925166955216932082205176917015 | PO_3557833493337258949730663 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "spc_rs", "hash": "10836582361615146128432455081617435566716339810571091015443831947106675851004488784423763836055619067254675954272729079464389889697641368168739113561436938", "id": "MD_1083658236161514612843245"} | MD_1083658236161514612843245 | null | [
"main_gst_gap_22_pbesol_cryst_spc_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs",
"refitted_gst_gap_22_pbe_cryst_spc_rs"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 587354665540163094379364466377277794855040328313154179489055068654630327579479042057059956743181937634515428359781547350314427038163164280563777134480248 | CO_5873546655401630943793644 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.23625529319304173,
0.21859567629705223,
0.545149030509906
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
]
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| null | [
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| true | null | null | -3,110.548344 | null | 0.178005 | 0.178005 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:39:14 | 9581294144120949440964578536657923716420018145337204538679625227492363087392981307496947651199256308202812051097683794299939875119263961932998473472908212 | PO_9581294144120949440964578 | {"config-type": "cryst", "masses": [72.63, 127.6], "sub-config": "rhom_GeTe", "hash": "6240288368831179278655173368742685790632375324962132005821718706446864372084663405911025033538700741186128949108475768232718859484959809394658838888233988", "id": "MD_6240288368831179278655173"} | MD_6240288368831179278655173 | null | [
"main_gst_gap_22_pbesol_cryst_rhom_GeTe",
"extended_gst_gap_22_for_efield_pbesol_cryst_rhom_GeTe",
"refitted_gst_gap_22_pbe_cryst_rhom_GeTe"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:03 | 6078806497502139828417201166448866450605775373635631081528012686233484056771532905293853867551160026083668879346003835329894501771441453179622862095799733 | CO_6078806497502139828417201 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -253,039.844612 | null | 0.962049 | 2.757409 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:05:40 | 13290122220919637489639519812312906131394212779298509096433484779344627839336675937121175546865060671468906760784117131727975033951156918015331996683200461 | PO_1329012222091963748963951 | {"config-type": "iter_5", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "domain_iter", "hash": "12311335613912890739531929734913368786220262585580202921926661016051947719066128757377003240550986611948696238083633535607862156055420609081180952121780260", "id": "MD_1231133561391289073953192"} | MD_1231133561391289073953192 | null | [
"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:08 | 6116963968477294276531129050216048880309352163087835736638755483056278950361389156696831781906586413632946116981449135011598793221174394354919316601335685 | CO_6116963968477294276531129 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.23625529319304173,
0.21859567629705223,
0.545149030509906
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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52,
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52,
52
]
| [
"Ge",
"Te"
]
| [
0.5,
0.5
]
| 2 | 64 | [
[
11.537798,
0,
0
],
[
0,
12,
0
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[
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| true | null | null | -99,530.064165 | null | 1.090519 | 3.401906 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:20:42 | 11843108543049378497355838153660405193398396998195627343420846737242254203778784413141908181311997555727370597449248179484642227368360544709581887776320943 | PO_1184310854304937849735583 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "conv_rs", "hash": "9624115815842736299756762216758143347926999742949070348197221794226386202497808807651541591776229055172344496066172994823565668725550956951281333715772155", "id": "MD_9624115815842736299756762"} | MD_9624115815842736299756762 | null | [
"main_gst_gap_22_pbesol_cryst_conv_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_conv_rs",
"refitted_gst_gap_22_pbe_cryst_conv_rs"
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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:05 | 6124179894507018448685700291785791957090539618979068782419572592182361137090690762471445601073384789096179221270342859486863763937339188557980245245256056 | CO_6124179894507018448685700 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| 2023-12-19T00:29:51 | 620786471804777566594113612685359141067964405447002081134818860384126288678453787642528173772124077633447265346747110695475620676004178815744993928563986 | CO_6207864718047775665941136 | GST_GAP_22_refitted | [
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]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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]
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| 2023-12-05T20:21:05 | 6229174097716444245287512898276794289447075718042684593869422475780467818089934098543852939020101754019510466373379497243828570929652245149291248005007293 | CO_6229174097716444245287512 | GST_GAP_22_refitted | [
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]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -14,307.204733 | null | 1.178925 | 1.510337 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:12:17 | 7998449044044248100193349846823665060050705890901363013822570703485738981636254574515199266119600479914771338749864833130217079859805931829813800535169365 | PO_7998449044044248100193349 | {"config-type": "cryst", "masses": [121.76, 121.76, 121.76, 121.76, 121.76, 121.76], "sub-config": "conv_ele_cryst", "hash": "11376588209332849124881644127347225818768470654784049131706689891424440782678255600298876432210759186988575066593177855023956768212279145568357977314864761", "id": "MD_1137658820933284912488164"} | MD_1137658820933284912488164 | null | [
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| 2023-12-05T20:21:04 | 6274483388039712475731576750594995112668177170296547602991651729478567673915794303842303317359775642784831464459296764652788521553606434155359529203816543 | CO_6274483388039712475731576 | GST_GAP_22_refitted | [
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"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| true | null | null | -20,306.974213 | null | 0.000011 | 0.000014 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:33:19 | 7686749312154963723056381013739656938331962892460351614235866154342373674275930745455260398366674633296490999075713718738617906873206927748169984904337223 | PO_7686749312154963723056381 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63], "sub-config": "conv_ele_cryst", "hash": "6349183166439829241359451791502533074712440869256473258456018648667318164169179950672651230309823549421286466339458593859063558119778084253780002125786382", "id": "MD_6349183166439829241359451"} | MD_6349183166439829241359451 | null | [
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| [
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -83,970.375876 | null | 0.594373 | 1.435339 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:04:31 | 6945815233908214239464530564119790272878309223473745473557851353594971828203062600656388396913403715316772988152372712589487717702201180524065125631239389 | PO_6945815233908214239464530 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "spc_hex", "hash": "13235791543569574279385427345369658452407681141389265953886517022488712993886195517946376035610230732619538486345132643668941183458936963654831828295697861", "id": "MD_1323579154356957427938542"} | MD_1323579154356957427938542 | null | [
"main_gst_gap_22_pbesol_cryst_spc_hex",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_hex",
"refitted_gst_gap_22_pbe_cryst_spc_hex"
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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 64182963762775708363483924722948053532423637651131689725881213287668176221083268426574388613469783233836952749653552784395134470471194887370675829833837 | CO_6418296376277570836348392 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
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| [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Sb96Te144 | Sb2Te3 | A3B2 | [
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| true | null | null | -311,280.291327 | null | 0.703089 | 1.833321 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:45:25 | 2665163439139310884988432948157913986392387284837300239112733693882171149350516902476199343063826093209155234196004553684200924692688263095940695297584207 | PO_2665163439139310884988432 | {"config-type": "iter_6", "masses": [121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "domain_iter", "hash": "10777048120809009227309672395765308208465436897299362160231714591812677073762634094035286335286536364985928043125856410500708598918713462060854864646473292", "id": "MD_1077704812080900922730967"} | MD_1077704812080900922730967 | null | [
"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:08 | 6423373976761198358633916878380245302662473187129080485788328810294942746396067902550179005009000195819020132849938320329695418187636947023206709683228425 | CO_6423373976761198358633916 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.23625529319304173,
0.21859567629705223,
0.545149030509906
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -70,757.829041 | null | 1.329557 | 7.480955 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:27:51 | 3049556974605917908239532800553235877889982259540326618494585382432619161025802575581440398659676146127722355760371895278507116674207910561689028853844103 | PO_3049556974605917908239532 | {"config-type": "cryst", "masses": [121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "conv_rs_anti", "hash": "9545917685873755232706076819414589124029353635790581010898585223717593585140015633847016046926148245266444675016752871479267579174556784556962829062253352", "id": "MD_9545917685873755232706076"} | MD_9545917685873755232706076 | null | [
"main_gst_gap_22_pbesol_cryst_conv_rs_anti",
"extended_gst_gap_22_for_efield_pbesol_cryst_conv_rs_anti",
"refitted_gst_gap_22_pbe_cryst_conv_rs_anti"
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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:06 | 6433828729405563135410553630460682153294562635076965038546082685749690158775657473482921430908689221629785510134426268782194798301590039804121643080705085 | CO_6433828729405563135410553 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -115,131.078537 | null | 0.717514 | 1.507795 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:15:39 | 11512663945145948405014717250554327740449325374652674612644135996381603917481728448143092397531406906993021329133307883861297410179214058614520658356233786 | PO_1151266394514594840501471 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "spc_hex", "hash": "683301799929896267516051236814807644720990017798961280935293538531152424999143328536006861717775122615099718594716446691743820353277059558707142257144930", "id": "MD_6833017999298962675160512"} | MD_6833017999298962675160512 | null | [
"main_gst_gap_22_pbesol_cryst_spc_hex",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_hex",
"refitted_gst_gap_22_pbe_cryst_spc_hex"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:05 | 6445798095872084488235603883913739989176754790069374836175293700955924608152869802763233375901727856680847815464336928885385405327622778617262696138742562 | CO_6445798095872084488235603 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 6446424960924445498640564247432832717680586391861938058323606844052942469083093668882231119984417710603885262511793977228753202589358144508574906629241951 | CO_6446424960924445498640564 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| true | null | null | -501,832.772754 | null | 1.018697 | 3.094198 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:17:14 | 12388731628266642924941828190994836766668005514279458586466466717167870188378246746762132175592579159997320107608865674625743070915495975144901372649500372 | PO_1238873162826664292494182 | {"config-type": "iter_4", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "domain_iter", "hash": "1702397721185590815662992276908473029959667428296423359511831648838992171863577105406721435167465480228643159770884958141330881349739824807847728204151053", "id": "MD_1702397721185590815662992"} | MD_1702397721185590815662992 | null | [
"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 6462326744137287201747031599481661529189087959686145168487984964904319166952779801011422845189873526663193128822894823314455029290033391847938870890441577 | CO_6462326744137287201747031 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
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1
]
]
| [
3
]
| 2023 | [
0.23625529319304173,
0.21859567629705223,
0.545149030509906
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 6480735308593289404117745955998315201611304848320262723160356627787794540949716547569984131138039878313690414804482033721115834093859695058553307092551498 | CO_6480735308593289404117745 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
1,
1
]
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| [
3
]
| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
SbTe | SbTe | AB | [
51,
52
]
| [
"Sb",
"Te"
]
| [
0.5,
0.5
]
| 2 | 2 | [
[
20,
0,
0
],
[
0,
20,
0
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[
0,
0,
20
]
]
| [
[
8.7,
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10
],
[
11.3,
10,
10
]
]
| [
true,
true,
true
]
| [
1,
1,
1
]
| 3 | 104872137360807527241881884746686383995391167147420142093949088945645186257476361383848371404682269276709411029249242241598200527412474825723060063505409 | 1 | CASTEP | PBE | null | [
[
0.53978,
-0.00002,
-0.00001
],
[
-0.53978,
0.00002,
0.00001
]
]
| null | [
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[
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| true | null | null | -2,954.686996 | null | 0.53978 | 0.53978 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:16:25 | 4157026445638988864276229435741174589491490364682618449656236057833802449410635533211922176370957315984179306865498477641875423548222477644656561064086165 | PO_4157026445638988864276229 | {"config-type": "dimer", "masses": [121.76, 127.6], "sub-config": "dimer", "hash": "6515008115902207792427764327069119881771508369463765552696522573764978900341385467272551511968839744382490372532353233233123211419700549454455532510838416", "id": "MD_6515008115902207792427764"} | MD_6515008115902207792427764 | null | [
"main_gst_gap_22_pbesol_dimer",
"extended_gst_gap_22_for_efield_pbesol_dimer",
"refitted_gst_gap_22_pbe_dimer"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:03 | 6489953529959851557103645466489055394346637682803893899305310051277756500190592167437338926625812539029771630557784030759092474382031889163062091457898630 | CO_6489953529959851557103645 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.23625529319304173,
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]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
GeTe | GeTe | AB | [
32,
52
]
| [
"Ge",
"Te"
]
| [
0.5,
0.5
]
| 2 | 2 | [
[
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0,
0
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[
2.165,
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0
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[
2.165,
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[
0,
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0
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[
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| [
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true,
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| 3 | 4082465692257905347971942069297756383803276994718552467573123708066404614750429871997662985039141154926911993491444364673573625035543314633389239220553705 | 1 | CASTEP | PBE | null | [
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[
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]
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| null | [
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| true | null | null | -3,110.58072 | null | 0.166216 | 0.166216 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:23:28 | 2258012850778432212458236756914317151112286224415848985718408121707958873641006159040069965281469220099340026725983050642413069548377493184403771657322862 | PO_2258012850778432212458236 | {"config-type": "cryst", "masses": [72.63, 127.6], "sub-config": "rhom_GeTe", "hash": "6240288368831179278655173368742685790632375324962132005821718706446864372084663405911025033538700741186128949108475768232718859484959809394658838888233988", "id": "MD_6240288368831179278655173"} | MD_6240288368831179278655173 | null | [
"main_gst_gap_22_pbesol_cryst_rhom_GeTe",
"extended_gst_gap_22_for_efield_pbesol_cryst_rhom_GeTe",
"refitted_gst_gap_22_pbe_cryst_rhom_GeTe"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:03 | 6519657597537957562670482816164718486784204829893141945170645046006674587750627143193525522160473910772200896702559055080471398543400096508034538816006489 | CO_6519657597537957562670482 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
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1
]
]
| [
3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Te3 | Te | A | [
52,
52,
52
]
| [
"Te"
]
| [
1
]
| 1 | 3 | [
[
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0
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[
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0
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[
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| [
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| 3 | 5266048719118631279791736068159502012642740750116687988279024713584372264361099539688590100972998525098066002261812946702792318025537069442547354251016408 | 1 | CASTEP | PBE | null | [
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[
0.00443,
0.64014,
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[
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| null | [
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| true | null | null | -1,715.433324 | null | 1.046954 | 1.568341 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:18:48 | 5619191228375855687931185348792758172685141325256080321081660554331372956709718672272952763274896242180771074107458422087990118547784823318832952205683248 | PO_5619191228375855687931185 | {"config-type": "cryst", "masses": [127.6, 127.6, 127.6], "sub-config": "conv_ele_cryst", "hash": "12686753169410293216915183305074981836119636246803503793562969877295476398725106144809830780309443770147449043505381746569096111203910703982470812304423691", "id": "MD_1268675316941029321691518"} | MD_1268675316941029321691518 | null | [
"main_gst_gap_22_pbesol_cryst_conv_ele_cryst",
"extended_gst_gap_22_for_efield_pbesol_cryst_conv_ele_cryst",
"refitted_gst_gap_22_pbe_cryst_conv_ele_cryst"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 6536641156877198150566714855848202354369247855729281338717938225247825228607825045254027657027208804459740208241598101352130565376815717816984999638215615 | CO_6536641156877198150566714 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
1,
1
]
]
| [
3
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| [
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"main_gst_gap_22_pbesol_iter_2_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_2_standard_iter",
"refitted_gst_gap_22_pbe_iter_2_standard_iter"
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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:05 | 6658509268358604314029132835454638993794746433440209119705255102262000365845581342195784129165005492342759996312362816815417657238184087163650530884276739 | CO_6658509268358604314029132 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 6737975999113668990250286821188860998940783916892792617509757423618370408630627687496901143598202552980652912824470986030221435854690953146571580750317360 | CO_6737975999113668990250286 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
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1
]
]
| [
3
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| 2023 | [
0.23625529319304173,
0.21859567629705223,
0.545149030509906
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| [
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| [
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"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 6759271385707700189481116607033542872551536564443495534671255311478753072238241697113489657043474259438274431041820031665626517968395351666655717621589611 | CO_6759271385707700189481116 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| [
3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| true | null | null | -30,881.9139 | null | 0.813169 | 2.357614 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:29:17 | 1790684126987097294686465816516947510026073748222229246575182374050615229667870403920597103992103177817264788868097954129536408142097035280881545965794000 | PO_1790684126987097294686465 | {"config-type": "cryst", "masses": [127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "spc_ele_cryst", "hash": "10482341115092695104624036464431580998332388409744383351399034763727849966709074052587122925035349033589465704752437318101099927297656895760288257555499527", "id": "MD_1048234111509269510462403"} | MD_1048234111509269510462403 | null | [
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| [
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| 2023-12-05T20:21:04 | 6834006906891710437991440708841721180288074436474305917029522669640368890312634072016268482592725729053507477684090661871702142892234186476187014272606205 | CO_6834006906891710437991440 | GST_GAP_22_refitted | [
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]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
SbTe | SbTe | AB | [
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| [
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| true | null | null | -2,951.682182 | null | 17.66751 | 17.66751 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:13:52 | 4543724174472266303830966623534738098946966937501907904804033183664539904219663321414165815239195041682821426239598784684704789336932059858536637266227689 | PO_4543724174472266303830966 | {"config-type": "dimer", "masses": [121.76, 127.6], "sub-config": "dimer", "hash": "6515008115902207792427764327069119881771508369463765552696522573764978900341385467272551511968839744382490372532353233233123211419700549454455532510838416", "id": "MD_6515008115902207792427764"} | MD_6515008115902207792427764 | null | [
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| [
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| 2023-12-05T20:21:03 | 6844402682038585652057597603337263929611763558128813380931121785269914819980417406867901726470313161023541280316104404188122786507729043943326509835531017 | CO_6844402682038585652057597 | GST_GAP_22_refitted | [
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"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| [
3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Sb72Te108 | Sb2Te3 | A3B2 | [
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"main_gst_gap_22_pbesol_cryst_spc_rs_anti",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs_anti",
"refitted_gst_gap_22_pbe_cryst_spc_rs_anti"
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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:06 | 6847247296010751826783842896090041426530959441079668683356180475216238448539524572473000806909537987456783272244975804663542365897448272557957608349287701 | CO_6847247296010751826783842 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| true | null | null | -3,110.596736 | null | 0.331627 | 0.331627 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:36:30 | 7207646863196478619255334547366748825337754326824433423886989119415141685012557155172007087172488498237207957876077386754135722808880678551817407350381503 | PO_7207646863196478619255334 | {"config-type": "cryst", "masses": [72.63, 127.6], "sub-config": "rhom_GeTe", "hash": "6240288368831179278655173368742685790632375324962132005821718706446864372084663405911025033538700741186128949108475768232718859484959809394658838888233988", "id": "MD_6240288368831179278655173"} | MD_6240288368831179278655173 | null | [
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 6994881282177142200641942082647829867754749126686706797588361861107076986044513823344058390397163577580054695231596853309219128340984166650359362321724903 | CO_6994881282177142200641942 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| [
3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_2_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_2_standard_iter",
"refitted_gst_gap_22_pbe_iter_2_standard_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:05 | 7067782458570944166592431013329964992694637631276585543227535212760175114410531753487590813361983207882392063042114273959256616869148571090571085475195343 | CO_7067782458570944166592431 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_2_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_2_standard_iter",
"refitted_gst_gap_22_pbe_iter_2_standard_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:05 | 707319579838612264717521921240102635658794794036416407928320524740615882046416460721550329702952384687739176499542292352295173762036947625944556513381266 | CO_7073195798386122647175219 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:09 | 708810200596175236276818821968075622792812269510457143890830431097655120493324960065875970198428429887734090795597577191846078032252290965845216946610821 | CO_7088102005961752362768188 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
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| [
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
GeSb4Te7 | GeSb4Te7 | A7B4C | [
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| [
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| true | null | null | -16,081.204897 | null | 1.473046 | 2.886341 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:42:47 | 8931644782319502432672439657808269722302784362456675481821801480198895119052465646046141195111429481682467704675682401812992106897097459243088228088411344 | PO_8931644782319502432672439 | {"config-type": "cryst", "masses": [72.63, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "MP", "hash": "6670849809936508038684425361296079389000744096582121330620801003639495202192562758442347602368425823929630316546379907427620389959176207587813213519977173", "id": "MD_6670849809936508038684425"} | MD_6670849809936508038684425 | null | [
"main_gst_gap_22_pbesol_cryst_MP",
"extended_gst_gap_22_for_efield_pbesol_cryst_MP",
"refitted_gst_gap_22_pbe_cryst_MP"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 7120693112067924856685387342474261103306687546445654801118159369899491690613336628778367640200374943390756088618552280357415188032327568767413263020544343 | CO_7120693112067924856685387 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Ge72Sb48Te144 | Ge3Sb2Te6 | A6B3C2 | [
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"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 7134782065436258282872021569703715418505375315813305890555713573477780954807848889269863019363867316227781122904004074593459175667742588131757363831035970 | CO_7134782065436258282872021 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 7153221969910283851121691940445651181407860652086416496613685353433878471852308295208427758319915624508370508136821019121813104339175947233636166369639016 | CO_7153221969910283851121691 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| [
3
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| 2023 | [
0.23625529319304173,
0.21859567629705223,
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]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -70,770.182492 | null | 0.504439 | 1.010377 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:27:16 | 5406469243119768735810094455321321722122823897167491871842046428061692334140233742866593596077688637981389918883227464111018427221701277310503071370004315 | PO_5406469243119768735810094 | {"config-type": "cryst", "masses": [121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "conv_rs", "hash": "12280937211562870598988830304113896895029527292819095523702273142655891132438662194395698138737645990536932281696951890820562476760864781705014704001989842", "id": "MD_1228093721156287059898883"} | MD_1228093721156287059898883 | null | [
"main_gst_gap_22_pbesol_cryst_conv_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_conv_rs",
"refitted_gst_gap_22_pbe_cryst_conv_rs"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:05 | 7157153469962527450787105065199880118289779355560275631873601195479389662562026007314106159699225640495823602403068362554241164586034781513371818900124853 | CO_7157153469962527450787105 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
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| [
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| 2023-12-05T20:21:06 | 7163463877494071365231408808163959472248384557443191367789997527698174468618626227556606342493370568769207484480341920848988011701116502382453796508859524 | CO_7163463877494071365231408 | GST_GAP_22_refitted | [
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"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_cryst_spc_ele_cryst",
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| [
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"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -70,755.21406 | null | 1.881863 | 6.115612 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:31:45 | 2232404525664544002467735971867669101054069985950710662036230677561703438550877174235933082309702526521680530193980637571887042321879730061175788072957419 | PO_2232404525664544002467735 | {"config-type": "cryst", "masses": [121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "conv_rs", "hash": "12280937211562870598988830304113896895029527292819095523702273142655891132438662194395698138737645990536932281696951890820562476760864781705014704001989842", "id": "MD_1228093721156287059898883"} | MD_1228093721156287059898883 | null | [
"main_gst_gap_22_pbesol_cryst_conv_rs",
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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:05 | 7195746164971393831082760564666820820981190751195280806013794809132334774751799344124305043892062203898198246242186233892098809063547531274856503284962749 | CO_7195746164971393831082760 | GST_GAP_22_refitted | [
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"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -311,273.596502 | null | 0.846702 | 1.955715 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:44:11 | 8991789331047438268179762954638448526384511166070387007521624405924730336679868456075751895571426559521596846482071022330372840029740128622048475387978133 | PO_8991789331047438268179762 | {"config-type": "iter_4", "masses": [121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "domain_iter", "hash": "5954239610862091555295430080115201475688165940077765239296964689702804334855777200589262988210634575458034564511937961381190945390618786672042931517012031", "id": "MD_5954239610862091555295430"} | MD_5954239610862091555295430 | null | [
"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 7218166103546858377902500131278959311165015072625670757167798870797721945552215904848075597340951319991796771028858428991855674184788366138991547828300960 | CO_7218166103546858377902500 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.23625529319304173,
0.21859567629705223,
0.545149030509906
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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52
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| [
"Ge",
"Te"
]
| [
0.5,
0.5
]
| 2 | 64 | [
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12,
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0
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0
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"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:08 | 7259095824319702878262704278506960064097386480145888716132466945490022722531439752445022376162866943831258021862871998555439097432136090438738677960383353 | CO_7259095824319702878262704 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:09 | 7263420555119435796328828503860683314783844177469807759938853310231492807073325048204664459238796546446058031637092148020106509971934959084672960123158377 | CO_7263420555119435796328828 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_3_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_3_standard_iter",
"refitted_gst_gap_22_pbe_iter_3_standard_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 7305046404164778826481778786401954337720560457885299608980849554392297577112072641640650957297822047131439222212178202648000368818136541259193572038913966 | CO_7305046404164778826481778 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_cryst_spc_rs_anti",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs_anti",
"refitted_gst_gap_22_pbe_cryst_spc_rs_anti"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:06 | 7319333291111516720718078241735654779446910422563103858447245949451146235653640150189587744054011770050149293005555081215798842017735297404539381466155504 | CO_7319333291111516720718078 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_cryst_spc_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs",
"refitted_gst_gap_22_pbe_cryst_spc_rs"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 7407284463837868312352741475289955152054933355551714848031626509098911746795113654917429593855619206975229314410021236286764464828105216730903797627780970 | CO_7407284463837868312352741 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
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1
]
]
| [
3
]
| 2023 | [
0.23625529319304173,
0.21859567629705223,
0.545149030509906
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Ge8 | Ge | A | [
32,
32,
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32,
32,
32,
32,
32
]
| [
"Ge"
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"main_gst_gap_22_pbesol_iter_2_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_2_standard_iter",
"refitted_gst_gap_22_pbe_iter_2_standard_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:05 | 7490479048821254208506070068077625921050578660768560896724879353323662864551766984886467623024500782349506891259397052311246258188168905520867420485552534 | CO_7490479048821254208506070 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| [
3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_1_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_1_standard_iter",
"refitted_gst_gap_22_pbe_iter_1_standard_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 7492438017233916166561734066145021930029496000324269873058267359367782613346336415618159898739627612098407934729888354573894286459797942281639216705673665 | CO_7492438017233916166561734 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 7497561957330455430483135773578294203164869051180016340465749671675139329457181382858810867626725900054020810970337743490573810162132327205359062947417336 | CO_7497561957330455430483135 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| [
3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 7521221293870874063524230253692726677586695510502911015631385192873174010578159184746218104510774032251113537249599799928779802017303604140335122363464984 | CO_7521221293870874063524230 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
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"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
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| [
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"DS_jy3ylaf48xg3_0",
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| 2023-12-05T20:21:04 | 7553223448692388707152712414789925637346683564313949257383813207719223627615228855706648482695680671544285238330856264878289872051429457332191026587916020 | CO_7553223448692388707152712 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -189,766.021701 | null | 2.340675 | 8.910584 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:23:09 | 164102325413668421577845995687234554852999825631531643585956398358167471244672254823197366531657039675633135716110104861802694173214689349428004610934969 | PO_1641023254136684215778459 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "spc_hex", "hash": "4849465936302868063207162086697408080084481861245939143826428979056038334202629119467156448264109850464082774563506243465960103728109761004678584889493250", "id": "MD_4849465936302868063207162"} | MD_4849465936302868063207162 | null | [
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| [
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| 2023-12-05T20:21:05 | 7559617828352917430042642910120572510666997295247744932875686702750062854825166156328276747150706199027294674617347394880879852779136449974274794869901572 | CO_7559617828352917430042642 | GST_GAP_22_refitted | [
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"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Ge3Te3 | GeTe | AB | [
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| true | null | null | -9,331.117338 | null | 0.628903 | 0.907775 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:23:36 | 12208739377020452710491320061273729304320715336781758276425439743459346029776590036743947472059921802498186309708488382981179510950592632743504196280485002 | PO_1220873937702045271049132 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 127.6, 127.6, 127.6], "sub-config": "conv_hex", "hash": "2416211705896189987138528861485508602154979060236222047577057132996305770989216649327016136327184122990563127560643768736656043656224695152297713666452863", "id": "MD_2416211705896189987138528"} | MD_2416211705896189987138528 | null | [
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| [
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"DS_jy3ylaf48xg3_0",
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| 2023-12-05T20:21:04 | 7659481281317581882684711887242131519745590499940781896179092356554254893217190151541290587521750944941110934570987604039179884563463553293516901334144248 | CO_7659481281317581882684711 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| [
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]
| 2023-12-05T20:21:06 | 7779579943561819960542408835863878567082683621383904802939158060211997584231168541753322925535225109632197710082667247313027301269704574085380039004621713 | CO_7779579943561819960542408 | GST_GAP_22_refitted | [
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"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Ge2Sb2Te5 | Ge2Sb2Te5 | A5B2C2 | [
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| true | null | null | -12,705.447498 | null | 1.940147 | 6.015756 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:27:18 | 4228450392732700732799167258245199044654978304519982529471797561767229839564892590448953046585762789946195629773386805483929846971058929568784129169801094 | PO_4228450392732700732799167 | {"config-type": "cryst", "masses": [72.63, 72.63, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "MP", "hash": "4240854710988803360310559471416294394280030664544546936774491095864290097461567268169237713745848429346332378023847517699902983002043750592705148718026549", "id": "MD_4240854710988803360310559"} | MD_4240854710988803360310559 | null | [
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"extended_gst_gap_22_for_efield_pbesol_cryst_MP",
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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 7798273709129010856772436077092137555663721364492244868839288893725821635337674684317274186739928592701656187239240442279081807776288469899940909219811155 | CO_7798273709129010856772436 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
GeSb4Te7 | GeSb4Te7 | A7B4C | [
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| [
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| [
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| 2023-12-05T20:21:07 | 7878313942862294436034388093909531894258084006584884066615979887268367689645640297590476841923379795982289460514251584473856107840596256600201179055282620 | CO_7878313942862294436034388 | GST_GAP_22_refitted | [
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
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"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 7957137999497776452878140228508739121638358154522665986581069776828258558702632334660528797056443796417388309583280033687069704331773970805112700842990768 | CO_7957137999497776452878140 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -30,881.854852 | null | 1.074149 | 1.075155 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:55:21 | 7849376722698115302489917143209547476511154639464122032207894622021148455261827839614391172733353388197656226490778839614543862196547147466924033036176384 | PO_7849376722698115302489917 | {"config-type": "cryst", "masses": [127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "spc_ele_cryst", "hash": "10482341115092695104624036464431580998332388409744383351399034763727849966709074052587122925035349033589465704752437318101099927297656895760288257555499527", "id": "MD_1048234111509269510462403"} | MD_1048234111509269510462403 | null | [
"main_gst_gap_22_pbesol_cryst_spc_ele_cryst",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_ele_cryst",
"refitted_gst_gap_22_pbe_cryst_spc_ele_cryst"
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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 7979253218419880381786295889388323806423845197217011337139159395598799740609883356861782006492189062662824801376962427856840487094018447965793841425359179 | CO_7979253218419880381786295 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_cryst_spc_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs",
"refitted_gst_gap_22_pbe_cryst_spc_rs"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 8021594362282785498516828568121393186733314076853029258231076434811676596104187707173456045778061580515781060442273257140871475949385714593359109580978786 | CO_8021594362282785498516828 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_cryst_spc_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs",
"refitted_gst_gap_22_pbe_cryst_spc_rs"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 8034162167326032684763412093287481645115651934251840627228658056402655770979351050272027073104344261603828491825783917931982547940648623084153916300026059 | CO_8034162167326032684763412 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| [
3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 8045126664731895695877616330613628542045422771040307181054593241902634319192044167225414412770859715332115637842780686050908647888750037989999413648558176 | CO_8045126664731895695877616 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 8095169426345193295530167034814869854387352625708742866397035488554578414286726156648693085872806954019646691161038296218301115538339528519813182754506140 | CO_8095169426345193295530167 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
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| 2023-12-05T20:21:05 | 81808710371624587284234869200113010801056056801854112932592675554982827214685381981218054814070397030492927349253840368982711942622532193576208576396500 | CO_8180871037162458728423486 | GST_GAP_22_refitted | [
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
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"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:08 | 8245241286447527829913895121670526546976476161920182287554260079188837902564061793205293030839651612541257658440914284472278791375268624098446781234008069 | CO_8245241286447527829913895 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
1,
1
]
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| [
3
]
| 2023 | [
0.23625529319304173,
0.21859567629705223,
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| [
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]
| 2023-12-05T20:21:07 | 8270847343421360786429300941169178284268347874047699612236990949290035084411621791792053788364902992084379209191019918731261851891313627703747630080361491 | CO_8270847343421360786429300 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Sb4 | Sb | A | [
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| [
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1
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| true | null | null | -9,537.69917 | null | 0.332976 | 0.426247 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:07:39 | 4060051529896567301342829730332068065256334714075157076994169011349344366327536782245229374089169703468800622934577370935569349910566699232465409689125981 | PO_4060051529896567301342829 | {"config-type": "cryst", "masses": [121.76, 121.76, 121.76, 121.76], "sub-config": "MP", "hash": "10708469732072958718894436945396842459064405508219097911751311763812830399053117687400639804661591936716780978275210903190947838316693035825745598475987403", "id": "MD_1070846973207295871889443"} | MD_1070846973207295871889443 | null | [
"main_gst_gap_22_pbesol_cryst_MP",
"extended_gst_gap_22_for_efield_pbesol_cryst_MP",
"refitted_gst_gap_22_pbe_cryst_MP"
]
| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 8339761523448250023479957748591051055656739990247274255946859337463131600102057826595545789070244796302261048363179617880285208656271656867778173861869823 | CO_8339761523448250023479957 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
GeSb4Te7 | GeSb4Te7 | A7B4C | [
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51,
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52,
52,
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52
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| [
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| true | null | null | -16,082.127778 | null | 0.788645 | 1.23227 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:27:15 | 12279805031906442369016821376634010292243889820371052414688400671704470863432629687734808229754611906336319196453099129261733752512487407025369255250841643 | PO_1227980503190644236901682 | {"config-type": "cryst", "masses": [72.63, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "conv_hex", "hash": "11606914213497341411008595714685281519729200534343730250725059374261891914615845843530564979381996516452893744129228585192303421822938486955566716032194708", "id": "MD_1160691421349734141100859"} | MD_1160691421349734141100859 | null | [
"main_gst_gap_22_pbesol_cryst_conv_hex",
"extended_gst_gap_22_for_efield_pbesol_cryst_conv_hex",
"refitted_gst_gap_22_pbe_cryst_conv_hex"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 8355973166313503550673704993390240883794891176061200915494157520284519237946117434615647212009278193941065137735829411503561831074853730640223588259175697 | CO_8355973166313503550673704 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
SbTe | SbTe | AB | [
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52
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| [
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]
| [
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| 2 | 2 | [
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[
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| [
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| true | null | null | -2,949.988287 | null | 0.30637 | 0.30637 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:10:16 | 12442158653939459333794082785369945200804557611922451188754223900204872393992539147321610399059474501041366639766222089413827034175292828298500694297139498 | PO_1244215865393945933379408 | {"config-type": "dimer", "masses": [121.76, 127.6], "sub-config": "dimer", "hash": "6515008115902207792427764327069119881771508369463765552696522573764978900341385467272551511968839744382490372532353233233123211419700549454455532510838416", "id": "MD_6515008115902207792427764"} | MD_6515008115902207792427764 | null | [
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"extended_gst_gap_22_for_efield_pbesol_dimer",
"refitted_gst_gap_22_pbe_dimer"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:03 | 8366424380580296351314495929063317194785167373846373951838431273664137843587303280694861670933930511861648035062770844817621788285345556067618621521270103 | CO_8366424380580296351314495 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
1,
1
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]
| [
3
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Ge9Sb6Te18 | Ge3Sb2Te6 | A6B3C2 | [
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| [
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| [
true,
true,
true
]
| [
1,
1,
1
]
| 3 | 2876702365664144309821234945181858188770145735558893937539598471107337783579474359349489513675812502757811451455172556135875237811018806503505700163300632 | 1 | CASTEP | PBE | null | [
[
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[
-0.00003,
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| null | [
[
-0.07635135063798354,
-0.000003450553886359065,
0.0000024236033249426767
],
[
-0.000003450553886359065,
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]
| true | null | null | -47,453.404757 | null | 0.207493 | 0.54238 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:57:35 | 9810860625056064357087080492069381243612148325990575168979288143962324058894598969732662003243521881255958869125533201492224291447088315385003828503855680 | PO_9810860625056064357087080 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "conv_hex", "hash": "3671207608404505370682589606096776184028544354012001044982517601694972033807052280135612196966234880039393893029496696662628098506552426897096802559474888", "id": "MD_3671207608404505370682589"} | MD_3671207608404505370682589 | null | [
"main_gst_gap_22_pbesol_cryst_conv_hex",
"extended_gst_gap_22_for_efield_pbesol_cryst_conv_hex",
"refitted_gst_gap_22_pbe_cryst_conv_hex"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 8372273029205403023327197946312370512004756536752373591280132085037455094523770854347640234244919115075027400670438559593580853291879768034088524773003673 | CO_8372273029205403023327197 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.23625529319304173,
0.21859567629705223,
0.545149030509906
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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