chemical_formula_hill
string | chemical_formula_reduced
string | chemical_formula_anonymous
string | atomic_numbers
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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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| [
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| [
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| null | [
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| true | null | null | -60,913.244984 | null | 1.910961 | 5.635775 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:31:47 | 10748926142092144222338414394452985275915154719498982614438217371910367902410122561058295598446568204287574212245085479143315735893947580141763411170178352 | PO_1074892614209214422233841 | {"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], "sub-config": "MP", "hash": "11866058122228346954156734186532938803390128345757456036454223089724601513710565740270638902699779000674816554979282675272247382687626689395943307742250178", "id": "MD_1186605812222834695415673"} | MD_1186605812222834695415673 | null | [
"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"
]
| 2023-12-05T20:21:07 | 6645984738808653161873203865763324642265660909748309075747286498352261974106415470241614161200561385559861137856856323239060423972144324067246368312128276 | CO_6645984738808653161873203 | 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,
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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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| [
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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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| 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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"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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"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 | 6757227013164120565161274388178507840744803249264707957322328708546963758586632268609363371738804155751930984951324089245604061139495267237017590377225521 | CO_6757227013164120565161274 | 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"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:08 | 6774890868533758277695185657035575844639802579832588901101702956382615268996815458374800645269769013678326655177997886929848282132153578110422181849954449 | CO_6774890868533758277695185 | 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_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"
]
| 2023-12-05T20:21:05 | 6798966072252787386648937672762317556753844669267730690117063675730507791875311378190536372645471411032297018702135385433147250792573126228138130125697242 | CO_6798966072252787386648937 | 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_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"
]
| 2023-12-05T20:21:05 | 6820872219673294075498815063129113060606902580121514885935330254504661030983288670342184005951861126201734877001515416875558003040080121978931068442262645 | CO_6820872219673294075498815 | 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 |
GeTe | GeTe | AB | [
32,
52
]
| [
"Ge",
"Te"
]
| [
0.5,
0.5
]
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| true | null | null | -3,110.512653 | null | 1.035331 | 1.035331 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:11:01 | 3040847980311880530694496548894512001988048216778422324927014810364009783203226350064730773793870995381386986464803450165271943770678287124757850733025463 | PO_3040847980311880530694496 | {"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 | 6931152473391822319275211205806142624179344580983697668503678880268051792114063841206847613176956539583990739929499740877696323599393531366984783058135085 | CO_6931152473391822319275211 | 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_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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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:08 | 6979208759792113693285611778954922882455819111752086369806538390912967448183436525717566459909380540048316632413478883579296255742827551588919670168844198 | CO_6979208759792113693285611 | 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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| [
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"DS_jy3ylaf48xg3_0",
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]
| 2023-12-05T20:21:08 | 6985145829098039712612813313432127071831360960362352299497580909535627153672081789632121482083156413712258019891774222331108417002088832471149815523468789 | CO_6985145829098039712612813 | 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 | [
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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
]
| [
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| true | null | null | -1,715.723694 | null | 1.433241 | 2.141752 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:56:45 | 4185677588563788933879435380844632114165655961047720825145144326437316903652450423808305627585186048829411129702417180565271769760994891797779618509661501 | PO_4185677588563788933879435 | {"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 | 7011155383590697009516728585471291346014744335577731355589405659059261634900872761463863248722793039145516955437588090357201558113865380070019838294258880 | CO_7011155383590697009516728 | 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
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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 |
Te3 | Te | A | [
52,
52,
52
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| [
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| [
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| true | null | null | -1,715.924474 | null | 0.633359 | 0.803876 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:31:14 | 11224433625911563300729589286936371779069044205895591497812851590072827721111926658570601931677296313829495337185415349504946706519930301619644155199467313 | PO_1122443362591156330072958 | {"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"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:04 | 7016965418224966131958285596811944417201556902254340395616859864873684533783249060619848890785805679771637792968402790958770330512234537581168295684649625 | CO_7016965418224966131958285 | 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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| true | null | null | -257,280.568302 | null | 0.614931 | 2.403505 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:09:19 | 11792278720540050134091800154703533090497125356167012084319702689676074064068047042838392654531281129111243675594019559734465634191703089458422790413012585 | PO_1179227872054005013409180 | {"config-type": "iter_3", "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, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 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], "sub-config": "standard_iter", "hash": "6235621281652257067515806524771189880660568947188803210709075068658484426765697155898000998256756502828643678114159040875952751474135307581017965249216066", "id": "MD_6235621281652257067515806"} | MD_6235621281652257067515806 | null | [
"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 | 7084089301430563806044477591179274817311033317155569449868188667593131447185640022769000542056966889423006049094712643302061493191875817634071607238946514 | CO_7084089301430563806044477 | 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 | 7110925839293324400257422747463145572184077439984904327202180519650820424067432832871355833077674457083353525293519788651529964524567738508865379368995752 | CO_7110925839293324400257422 | 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_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 | 722167290098865132781867219099677192740219599649941147731970552261027500666632691071256218277498704291357948229523750578378720086014040960820465351965287 | CO_7221672900988651327818672 | 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 |
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| true | null | null | -345,420.823023 | null | 0.797923 | 2.864407 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:22:52 | 9906733377972612629368957407529111154843053454978567774960176055689412459607749670323889189955287249871763572401845927433742285508814003465365877035702742 | PO_9906733377972612629368957 | {"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, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 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": "11537440991536537754037912655592939354506471664412049949820742337379677114227315085678059548225781043192209954397305076699925857114903568900171002734974579", "id": "MD_1153744099153653775403791"} | MD_1153744099153653775403791 | 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"
]
| 2023-12-05T20:21:07 | 7222739122495849900454751641902367162239107353988689735791101058158998174478422110205387451160018038508833763937438538089096779892517597262999382789200826 | CO_7222739122495849900454751 | 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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| [
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"DS_jy3ylaf48xg3_0",
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]
| 2023-12-05T20:21:05 | 7225349241048098398815389487359354604675389624848261023652767625202177471198413069630778348562568922075892180423686553597602939610079521635196147948842639 | CO_7225349241048098398815389 | 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
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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 |
GeTe | GeTe | AB | [
32,
52
]
| [
"Ge",
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| [
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0.5
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| true | null | null | -3,110.569317 | null | 0.210296 | 0.210296 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:04:14 | 10141926148914801673546339825922368539239952636472771577307873176858329374412960188125704784261849328496358441426337146044205273938942054312312716051053580 | PO_1014192614891480167354633 | {"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 | 7248176566192213707555262590498316328486505671165543182020530413616032721334133734838373499504209137506785344139581260793112581099651426561230103507738051 | CO_7248176566192213707555262 | 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 | [
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 |
Ge2 | Ge | A | [
32,
32
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| [
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| [
1
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[
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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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"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"
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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 7324283693342468427029865367033416326923005206535374621221411123398644264137956500186820187976200302440832997243203389763816295665923622695559674873406848 | CO_7324283693342468427029865 | 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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"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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| 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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"Evan Ma",
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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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| 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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| 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 | 7662192353407816419102100814770956500320262591158799903816151573841218069254932543008830202694678005927168289121232447295559142434147937095580995055897571 | CO_7662192353407816419102100 | 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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| 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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| 2023-12-05T20:21:03 | 7687302509515507441761432052456809573888470036513341214604452949077947809810969314890775888707096484638951528389922726637870149675750890361831517077231071 | CO_7687302509515507441761432 | 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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"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
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"DS_jy3ylaf48xg3_0",
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| 2023-12-05T20:21:04 | 7722289222333081542151923983400595902864242271305199024176508631945220787207906162057866590633656840937306710620166523840737107737435462964652773298424667 | CO_7722289222333081542151923 | 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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| 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"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 7776279847022526243700146474483013509137600215766460651572784353703280273572619343867634938313672884135301513855802961474715977442270762562663005409976772 | CO_7776279847022526243700146 | 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 | 7781790010986265803731056494206264227797100842397310567959005284059775261583793926553190922159835180583360422239056752353960091510735588098740140769153187 | CO_7781790010986265803731056 | 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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| [
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| true | null | null | -30,869.134269 | null | 2.728603 | 10.436842 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:30:40 | 726426332155064526776598649151632900316051127606871762195967855887621245509788648939818236971406195307234054786695218403725594812359079178637633644111357 | PO_7264263321550645267765986 | {"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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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:04 | 7792495386687550529832558018335515607439703992509070847240417136682430835087764184436964592754310997699933571669974172576713012659452135967451635188841479 | CO_7792495386687550529832558 | 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"
]
| 2023-12-05T20:21:05 | 7806295990807382693554281839705291865939558074943566815250917572924481513593244256725061239286302571110219823920564517945667251320294507033354855135420728 | CO_7806295990807382693554281 | 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_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 | 784815627295460487952378328138535246362663514837038249344316622911621943622615137063943883355680670611863325933081924414639721381419228569694935375037290 | CO_7848156272954604879523783 | 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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| 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_spc_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs",
"refitted_gst_gap_22_pbe_cryst_spc_rs"
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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:06 | 8171491627777158937358099892521862788851481581184765996709107876501030973966355611902092182977655785062809290782409384019961909476323734462346420789229493 | CO_8171491627777158937358099 | 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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| true | null | null | -189,808.803739 | null | 0.398861 | 1.097415 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:28:47 | 3108968152209684286181625206341841569819758698528105382990185010826876802986187143733142765921746431433891585812936897629786455748120494572264504335918934 | PO_3108968152209684286181625 | {"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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"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"
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| 2023-12-05T20:21:05 | 8212697296944482755252256233187376767773931951343276235520668205427471664611718635922279001384110041099624165935872703070107890623159805972344134520548719 | CO_8212697296944482755252256 | 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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| true | null | null | -90,986.624566 | null | 1.330101 | 2.999196 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:21:20 | 5734236311600023381268048615477139070956466586168731293852490526128069582051837692774605822139358123278740991983487159134110611432266628008271616975944323 | PO_5734236311600023381268048 | {"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, 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": "11669784002062682397795376153279258639672969425680376277409657283026376321112150456094410281052842039439268899559747412146357774231457432252868563960545101", "id": "MD_1166978400206268239779537"} | MD_1166978400206268239779537 | 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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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:06 | 8216607140575929413935708620431001419680394550114530146688351920721039300268656068588209996199916895689082778155541098771504324627135408482492946492969532 | CO_8216607140575929413935708 | 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_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:07 | 8227739191185118356942338525275110149631795597417896334987018316052819778114694724845236661232646337608503973274805619288249681031545308261013910057512493 | CO_8227739191185118356942338 | 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 | -114,335.143762 | null | 2.025834 | 6.326711 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:43:55 | 9404844666250241067381872603732963877884502805040513595388168041478712366574088628263745075921401437119242941418203470382736014005522117979960227900409998 | PO_9404844666250241067381872 | {"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, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 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], "sub-config": "spc_hex", "hash": "1833062707632126152463528663644268874658489497539662541298359284453402049046387359673816135538780238189376492911875228133460742262582510363923115394402790", "id": "MD_1833062707632126152463528"} | MD_1833062707632126152463528 | 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"
]
| 2023-12-05T20:21:05 | 823982216949311025335577653290040661618942652365430945747938781661622837493021540098619019511637919000485693689034019915548413994203449481300407535301635 | CO_8239822169493110253355776 | 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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| 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_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 | 8313413382792688533787411340909329429228043513623171895487986985957902412421597618702694189292125371895149565297914234217208855766060749186860363741026923 | CO_8313413382792688533787411 | 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"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 831617592699536697419235784822684133472375555289426004885839178646117535569026501546322835513581709458973792405054868829234876871192094489638858658909357 | CO_8316175926995366974192357 | 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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| 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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| 2023-12-05T20:21:06 | 8381728277362783755853954021733818993016916258531420588712959946082887721327757302132664181810328964030568179022516589976216650212136733888427889411838311 | CO_8381728277362783755853954 | 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",
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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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GeTe | GeTe | AB | [
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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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"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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Te3 | Te | A | [
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| [
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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"
]
| 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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| 2023-12-05T20:21:04 | 8411517388617595785242927844730239459327057422978034849795506266009533354708421279201623998830897313465849982720374598067862502359651934992020186387238744 | CO_8411517388617595785242927 | 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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| 2023-12-05T20:21:03 | 8451968219430799797150337476759294281143392262255043505299255745449746494181178880028926428664284960228515986515741996657903054618549817566553732075948678 | CO_8451968219430799797150337 | 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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"extended_gst_gap_22_for_efield_pbesol_cryst_spc_hex",
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| [
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"DS_jy3ylaf48xg3_0",
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| 2023-12-05T20:21:05 | 8496954361674455004859503022523541396283642008652148823236213488327168277941569310890233126114939173559460214032506122252324903748832591131680362101542581 | CO_8496954361674455004859503 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
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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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| 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_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 | 850550821221431450836914860667383123341816786941711352942056622171115063187033141127274553749520585293977224228480660506251078709885027845878689897535941 | CO_8505508212214314508369148 | 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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| 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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"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 | 8690136131500763630220674117873234815954333975000224355437619498948512675223037554232630179295826158457429877979768011198153118000220272857395290528590474 | CO_8690136131500763630220674 | 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"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 8728393025875942374467342129032538980067925730129719575829494916456363610622517821510024000790365036498944537498459857419436026647104862846357305694201876 | CO_8728393025875942374467342 | 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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]
| 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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| 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 | -20,304.740293 | null | 2.493278 | 2.99618 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:12:11 | 5705757140848284439035247291315133812320117453511324623494911420129807290130094888532634898284915216016652730326120078602160337539813706045912012535459583 | PO_5705757140848284439035247 | {"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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| 2023-12-05T20:21:04 | 8835779838104451424938078942328144347010696945847279970292545690480907811269156326707684049209869622899461522375683847955684750878160775870698703076170362 | CO_8835779838104451424938078 | 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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Sb6Te9 | Sb2Te3 | A3B2 | [
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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 | 8884938677349189643175844517565820099945262987128953393658332439861010602317496670403604661521294486115119825739020662903842411915454103624189060390973088 | CO_8884938677349189643175844 | 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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"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",
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| 2023-12-05T20:21:05 | 8988801566283199923525332393510716146697268958580545942864655448610771030924583293519810026077022675483485879568630805351179298057760809558907238655294386 | CO_8988801566283199923525332 | 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 | -71,382.658582 | null | 0.477584 | 1.161215 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:19:36 | 4802926530474598945326241083208914859517102672553680212108533232536811146042270735679784301405017181852326717752389013936492281326218240674217550253529361 | PO_4802926530474598945326241 | {"config-type": "cryst", "masses": [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, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 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": "7583248193253721435988354141533753145514240242908978894587514962002194664664794927268285074587946246183169942703624997915635946341177724680919346281268912", "id": "MD_7583248193253721435988354"} | MD_7583248193253721435988354 | 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"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 9021129300200515628756283461583884309654711804803901471801608968535771592980507120736941737112858911645085756584318381405016137007720340863613568022927468 | CO_9021129300200515628756283 | 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 |
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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 | 9179484481064238513765252760475615132263999100076004033603726264059388849059095464450282406215256361587198569563695987862811038450323808166633374556117125 | CO_9179484481064238513765252 | 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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| 2023-12-05T20:21:04 | 9214105827700201311775480320566189121369124872622536145998030422338475828578359884176476196533525134478718154338033086314419728146590523737110969430558090 | CO_9214105827700201311775480 | 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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| 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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"refitted_gst_gap_22_pbe_cryst_spc_ele_cryst"
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| [
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| 2023-12-05T20:21:04 | 9272436986266864114116041859398218075071186580217339916414028628836100403392788981602160773311098343613012823603537856379710831254487589658427624124774157 | CO_9272436986266864114116041 | 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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| 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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| 2023-12-05T20:21:07 | 937416382369322560046130340221836007846364073341511010976389400574410863299642087168422602234974106441804222797522354121839292326932496679498712115539280 | CO_9374163823693225600461303 | 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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| true | null | null | -335,935.206574 | null | 0.588905 | 2.4359 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:48:13 | 11455701255400175990352742839145394766316413607733160878974230164942247223796663802700359339924453583754108379925747502166246018104095099995493489119503077 | PO_1145570125540017599035274 | {"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 | 9375290159542786463906826795933406568207506189784010179381204362801281815435075384801147729676645351234415382391175050068381044849850545199536614007965759 | CO_9375290159542786463906826 | 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
]
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| [
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 |
Ge54Sb36Te108 | Ge3Sb2Te6 | A6B3C2 | [
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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 | 9475923125468568534647323754358995951854754249827452310593110739032128095571414048044200934989383506481268278725576258373887670418169790714306109858508827 | CO_9475923125468568534647323 | 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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"extended_gst_gap_22_for_efield_pbesol_iter_3_standard_iter",
"refitted_gst_gap_22_pbe_iter_3_standard_iter"
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| [
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]
| 2023-12-05T20:21:05 | 952876834838483627468157721860727908215143808991547438990237440872214645118041519420070497151275833873326052700883639267000134647224886867944522626765563 | CO_9528768348384836274681577 | 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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| 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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"extended_gst_gap_22_for_efield_pbesol_dimer",
"refitted_gst_gap_22_pbe_dimer"
]
| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:03 | 9603123931620189426683756567807112541546725850822975492915072395962014413159689060216379395307484985614132476210113940476314274773356365928815340769864953 | CO_9603123931620189426683756 | 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 |
Sb6Te6 | SbTe | AB | [
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| [
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| true | null | null | -17,740.064728 | null | 1.58865 | 5.187587 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:34:34 | 11616028253665910112915111391686707401120649741702062413295745470701997881633821299538608689086820852253259688457468478212507074235214944334580549979762981 | PO_1161602825366591011291511 | {"config-type": "cryst", "masses": [121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "MP", "hash": "4275741023574137123122185753339649270607065339385900977771305811638412127363335787375676253456683463212131702163180486046814111828381999308143207348792451", "id": "MD_4275741023574137123122185"} | MD_4275741023574137123122185 | null | [
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"extended_gst_gap_22_for_efield_pbesol_cryst_MP",
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| [
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| 2023-12-05T20:21:06 | 963984152495429285086814816741185175956333517504951540031790904321223078294280370016694856371834945826274949724225493673029368088288227619055508192917649 | CO_9639841524954292850868148 | 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"
]
| 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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| 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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| 2023-12-05T20:21:06 | 9715680123816411685157948604738524020104283917094817004699635301531819115605563570407580401056637638085761194449800332433894204757162970189938632663908663 | CO_9715680123816411685157948 | 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_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"
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| 2023-12-05T20:21:07 | 9758018287264461545201861603294179109200220603335486275831623105626759061275536591124853982096986775640881985695416282990643920297949821259183153361348935 | CO_9758018287264461545201861 | 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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| 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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| 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 | 9948311227515598961301058310857967031437734273324346293277681842752240243380308067814488074628093627290996003603119092848092554712376807858525291482075489 | CO_9948311227515598961301058 | 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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"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"
]
| 2023-12-05T20:21:05 | 9964060046755958834508080811598532092300637440284053802445202614820006249399303885999672813452895607193699435746340522950266136580278626502361798554657934 | CO_9964060046755958834508080 | 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 | -246,422.035775 | null | 0.687779 | 1.843628 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:27:02 | 4769039292952578445055083234825165567835683664866590048186568302729113045213119088226680231902895649762763288121612300776358153895660498842300817258151518 | PO_4769039292952578445055083 | {"config-type": "iter_2", "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, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 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": "standard_iter", "hash": "4415168539254758589942931451969303721115222292080397924978179195697049209994957843001071003049866381675010346314412424454152226742193272429914947749831800", "id": "MD_4415168539254758589942931"} | MD_4415168539254758589942931 | null | [
"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 | 9982989684533609735712070475474725492768547207626724312130171324574191418853722520381044843321935441582885608022359253716014150852786599132591625260172584 | CO_9982989684533609735712070 | 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 |
Te3 | Te | A | [
52,
52,
52
]
| [
"Te"
]
| [
1
]
| 1 | 3 | [
[
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0,
0
],
[
-2.14872,
3.777013,
0
],
[
0,
0.204088,
5.967348
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| [
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[
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[
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2.95412194,
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| [
true,
true,
true
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| [
1,
1,
1
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| 3 | 10470471576153363125272366202402480478610879372164199482246655283025583875530167406380603806249554150655765572463793200640645851391316132119816491283130300 | 1 | CASTEP | PBE | null | [
[
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],
[
0.10552,
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],
[
-0.09823,
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| null | [
[
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[
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| true | null | null | -1,715.786233 | null | 1.328484 | 1.976646 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:42:25 | 7238057238472488987549112684903182373478978975564158469098355529313539267392332016024764225158267454956086571308018957805368445512906175033842608144960154 | PO_7238057238472488987549112 | {"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 | 9997369116173160472473352550252610963932876980598278423296932480995922207945885095499782469157100587557132965723876246148840003074548010862983543854282062 | CO_9997369116173160472473352 | 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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