chemical_formula_hill
string | chemical_formula_reduced
string | chemical_formula_anonymous
string | atomic_numbers
list | elements
list | elements_ratios
list | nelements
int32 | nsites
int32 | cell
list | positions
list | pbc
list | dimension_types
list | nperiodic_dimensions
int32 | structure_hash
string | multiplicity
int32 | software
string | method
string | adsorption_energy
float64 | atomic_forces
list | atomization_energy
float64 | cauchy_stress
list | cauchy_stress_volume_normalized
bool | electronic_band_gap
float64 | electronic_band_gap_type
string | energy
float64 | formation_energy
float64 | max_force_norm
float64 | mean_force_norm
float64 | property_object_metadata
string | property_object_metadata_id
string | property_object_last_modified
timestamp[ns] | property_object_hash
string | property_object_id
string | configuration_metadata
string | configuration_metadata_id
string | configuration_labels
list | configuration_names
list | configuration_dataset_ids
list | configuration_last_modified
timestamp[ns] | configuration_hash
string | configuration_id
string | dataset_name
string | dataset_authors
list | dataset_description
string | dataset_elements
list | dataset_nelements
int32 | dataset_nproperty_objects
int64 | dataset_nconfigurations
int32 | dataset_nsites
int64 | dataset_adsorption_energy_count
int64 | dataset_atomic_forces_count
int64 | dataset_atomization_energy_count
int64 | dataset_cauchy_stress_count
int64 | dataset_electronic_band_gap_count
int64 | dataset_energy_count
int64 | dataset_energy_mean
float64 | dataset_energy_variance
float64 | dataset_formation_energy_count
int64 | dataset_last_modified
timestamp[ns] | dataset_dimension_types
list | dataset_nperiodic_dimensions
list | dataset_publication_year
string | dataset_total_elements_ratios
list | dataset_license
string | dataset_links
string | dataset_doi
string | dataset_hash
string | dataset_id
string | dataset_extended_id
string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Ge2Te2 | GeTe | AB | [
32,
32,
52,
52
]
| [
"Ge",
"Te"
]
| [
0.5,
0.5
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| 2 | 4 | [
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[
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[
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| [
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| 3 | 5976787169610983979902255730438779417818061433903218150261123417038513278979033662043059889699634159070076248381359139166264971037474954338543879168316141 | 1 | CASTEP | PBEsol | null | [
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| true | null | null | -6,208.088828 | null | 2.416756 | 2.454683 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:38:31 | 2777030480296289476554945883353586900718425884373413332584128916103801342418311374694479706411941495895319548141429645078050280740248551898681075908215983 | PO_2777030480296289476554945 | {"config-type": "cryst", "masses": [72.63, 72.63, 127.6, 127.6], "sub-config": "MP", "hash": "12577178269503330384834064891329118702088415508764640690502115870652210234452744412793691596841873866160539642223316801161692052361723241522164901309411928", "id": "MD_1257717826950333038483406"} | MD_1257717826950333038483406 | 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"
]
| 2023-12-05T20:21:06 | 1661827210600402589224341304044352942040314776113314621460832324730154506265778554775951827796311026156909856027050744124561065328820660779260197974670208 | CO_1661827210600402589224341 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
1,
1
]
]
| [
3
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Ge9Sb6Te18 | Ge3Sb2Te6 | A6B3C2 | [
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32,
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32,
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32,
32,
32,
51,
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51,
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52,
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52,
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52
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| [
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| [
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| 3 | 33 | [
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[
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| [
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true,
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| [
1,
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| 3 | 7209059006651283421706256615162563093865093909074760024986434442719516948892525075102995122839613868694229620753531791218529371983764779681603102696706797 | 1 | CASTEP | PBEsol | null | [
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| null | [
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| true | null | null | -47,369.948165 | null | 0.587196 | 1.273384 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:01:21 | 3248698213714171577706587152479723632555933189315408968583974208885969840932553898478880958942123230202827683604356887606566018381444882505945981198565224 | PO_3248698213714171577706587 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "conv_hex", "hash": "3671207608404505370682589606096776184028544354012001044982517601694972033807052280135612196966234880039393893029496696662628098506552426897096802559474888", "id": "MD_3671207608404505370682589"} | MD_3671207608404505370682589 | null | [
"main_gst_gap_22_pbesol_cryst_conv_hex",
"extended_gst_gap_22_for_efield_pbesol_cryst_conv_hex",
"refitted_gst_gap_22_pbe_cryst_conv_hex"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 1662139218575584390084733856818003682968991806816684765027608546071715137046802394115611047800853003862202624135396453223313565608251303685887385588528642 | CO_1662139218575584390084733 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.23630478379677952,
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Sb8Te12 | Sb2Te3 | A3B2 | [
51,
51,
51,
51,
51,
51,
51,
51,
52,
52,
52,
52,
52,
52,
52,
52,
52,
52,
52,
52
]
| [
"Sb",
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]
| [
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| 2 | 20 | [
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| [
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| null | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 main training dataset for GST_GAP_22, calculated using the PBEsol 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 main training dataset for GST_GAP_22, calculated using the PBEsol 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_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 | 1709599902346171961953537757209434808691798717107525107978206157049031450907471427776284679784083407298706928818357368687686985763606467324233360849681703 | CO_1709599902346171961953537 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_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"
]
| 2023-12-05T20:21:08 | 1722587776142119778915981487864588146392225776916882815295458961580579188103865971822151676561382853751542162841778206004166107004783302208198028626181005 | CO_1722587776142119778915981 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
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1
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| [
3
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| 2023 | [
0.23630478379677952,
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| [
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| [
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"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 1732894899606498608531509710730946809829720281384999697022784112013813870000530067025741933247904530259543196137408296640195265381685296363644121928799355 | CO_1732894899606498608531509 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
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| 2023 | [
0.23630478379677952,
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Ge43Sb44Te108 | Ge43Sb44Te108 | A108B44C43 | [
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"main_gst_gap_22_pbesol_cryst_spc_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs",
"refitted_gst_gap_22_pbe_cryst_spc_rs"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 1739403779705031193475487264050901801992535076257935186183708287164169423442391719092873014612694107410539893687909954188436662228188345331890828772474934 | CO_1739403779705031193475487 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Sb2 | Sb | A | [
51,
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| [
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| [
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| 1 | 2 | [
[
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[
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| [
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| [
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| [
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| 3 | 279135756240664420099261101040261582339136953954103795547621380394201657777007909133780137507131599498327780334822719530393062269521807247706511810889555 | 1 | CASTEP | PBEsol | null | [
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| null | []
| null | null | null | -4,756.242034 | null | 0.01885 | 0.01885 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:12:06 | 4429417538463637720105072662721759671594830300779686101498583308091611480405536924155198000431488993584303650622400738568112798323402893115046109761925599 | PO_4429417538463637720105072 | {"config-type": "dimer", "masses": [121.76, 121.76], "sub-config": "dimer", "hash": "12749784377554201583291612343634491581939460251612151514457680212018420488370028909551115401499445089420899938851942171239271722616911573405893787570700809", "id": "MD_1274978437755420158329161"} | MD_1274978437755420158329161 | null | [
"main_gst_gap_22_pbesol_dimer",
"extended_gst_gap_22_for_efield_pbesol_dimer",
"refitted_gst_gap_22_pbe_dimer"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:03 | 1744273553230324730591430636018056766171256828767522551884594550218694208786924353403670186550447250868758264065292144712225375657630123096089193167876065 | CO_1744273553230324730591430 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
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| [
3
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| 2023 | [
0.23630478379677952,
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Ge36Sb72Te144 | GeSb2Te4 | A4B2C | [
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| [
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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 | 1751425565752417993617136421586357198704006360311383660723157109553420331173113385936051837443207740603991430103431365437710259645033960011160124430829505 | CO_1751425565752417993617136 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_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 | 1754406192955594229716101938525348929857972250633324452128950282717154251464431864497636989484547612251996481355841284870005145574872165614988289095444031 | CO_1754406192955594229716101 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
0.23630478379677952,
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 main training dataset for GST_GAP_22, calculated using the PBEsol 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 main training dataset for GST_GAP_22, calculated using the PBEsol 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 main training dataset for GST_GAP_22, calculated using the PBEsol 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 main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 1921607860117543786657680319199086970089137153306825530670115632982725222307355739985899248435377732641361005672689067826528904814970839111285378200150684 | CO_1921607860117543786657680 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
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| [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
GeTe | GeTe | AB | [
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| true | null | null | -3,106.248532 | null | 0.301358 | 0.301358 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:56:57 | 5428146186250927666974512664848853950082667232847567539696258656803584373796779881649813760333887063893710529871438145264291361037248503218429274368971981 | PO_5428146186250927666974512 | {"config-type": "cryst", "masses": [72.63, 127.6], "sub-config": "rhom_GeTe", "hash": "6240288368831179278655173368742685790632375324962132005821718706446864372084663405911025033538700741186128949108475768232718859484959809394658838888233988", "id": "MD_6240288368831179278655173"} | MD_6240288368831179278655173 | null | [
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| [
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]
| 2023-12-05T20:21:03 | 193165968498096986152965557035560679056170377998154719643540570367284190647951962393519529166123153086863376605505032489768520253804251258954006877065681 | CO_1931659684980969861529655 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
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1
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| [
3
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| 2023 | [
0.23630478379677952,
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
GeTe | GeTe | AB | [
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52
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| [
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| true | null | null | -3,106.464569 | null | 0.02849 | 0.02849 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:42:44 | 2027144186920311360312740321681496578600017991465861456595515604379855995100637335247254492630884121487975855400217863427650843588037755214575942802064520 | PO_2027144186920311360312740 | {"config-type": "cryst", "masses": [72.63, 127.6], "sub-config": "rhom_GeTe", "hash": "6240288368831179278655173368742685790632375324962132005821718706446864372084663405911025033538700741186128949108475768232718859484959809394658838888233988", "id": "MD_6240288368831179278655173"} | MD_6240288368831179278655173 | null | [
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"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 | 1936413493481245194032822049820773027727553779327658098678725000594265005037707285304026903990439924274691979456345598639799243470600386276317613582018996 | CO_1936413493481245194032822 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
1,
1
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| [
3
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| 2023 | [
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| 2023-12-05T20:21:04 | 1959706171082596002844053495253757544776983727861234182000673191104238242033000653485213574170455416992398600550948849159753607980460322904074857846625992 | CO_1959706171082596002844053 | GST_GAP_22_main | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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Sb6Te9 | Sb2Te3 | A3B2 | [
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| 2023-12-05T20:21:04 | 2003342675170214750423968081181561030804241731259965591544427331078790528035243729273383097815067948195566146955063722175853759454938260886628041911578246 | CO_2003342675170214750423968 | GST_GAP_22_main | [
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"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
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| 2023-12-05T20:21:04 | 2034046226038149881552431985284281792842275719479254132648908235149853606372920310996470893438427368267684427242275898760693816070674739067158328639240761 | CO_2034046226038149881552431 | GST_GAP_22_main | [
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]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| true | null | null | -12,682.270311 | null | 0.769039 | 1.119747 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:37:38 | 4864829026063817959189453943761574081155191012419502977589043675414590080745906215489167714031761143527096938455671800809408385654442567477200364597642799 | PO_4864829026063817959189453 | {"config-type": "cryst", "masses": [72.63, 72.63, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "conv_hex", "hash": "3736747359295474668976008674728693682234744152525329248995556077271058404753842235067000269318827100579604246377666184098831224454551029227862824894315935", "id": "MD_3736747359295474668976008"} | MD_3736747359295474668976008 | null | [
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| [
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| 2023-12-05T20:21:04 | 2055737022097030732976746585066193599872193194063561568222275431079838088384905550130485648428785851370686723657546870726281093229599338842064955836826813 | CO_2055737022097030732976746 | GST_GAP_22_main | [
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]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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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| 2023-12-05T20:21:09 | 2059058384005503009543473382804656676106026542222624022756746880297576873143068957805238091064814154476019177202115959984539318681666979460426763542943557 | CO_2059058384005503009543473 | GST_GAP_22_main | [
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"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| [
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| 2023-12-05T20:21:03 | 206016055018227360005154216812419479656964493388324404281853636783683090092240623404487205587072647261903559620462153657638684722004567713753979360570960 | CO_2060160550182273600051542 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_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"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 2064715261295035631575317482863081248908033336064007652190050192179556079231252507567794043988307248117413454647890646297008092146422939507091668631337618 | CO_2064715261295035631575317 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
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1
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| [
3
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| 2023 | [
0.23630478379677952,
0.21855465772221375,
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Ge27Sb54Te108 | GeSb2Te4 | A4B2C | [
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"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
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| [
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"DS_jy3ylaf48xg3_0",
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| 2023-12-05T20:21:03 | 2069363205741225513350182464029687486573830523429114002476633577989968007897808347368632536846213590011175971046523475435230314051246390427786357061698692 | CO_2069363205741225513350182 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_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"
]
| 2023-12-05T20:21:08 | 2070953727438121508505777420236529814712507938785071323169452154052073486658125590018036599244888459623761632188419919083455652725772853916042709284643147 | CO_2070953727438121508505777 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
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| [
3
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| 2023 | [
0.23630478379677952,
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
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]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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 | 216214792046789685638208311413503173533369153796262740011445604259030159254701693562220447600472627731496401678536416647459301628591368273189740801395158 | CO_2162147920467896856382083 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
0.23630478379677952,
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| true | null | null | -275,359.59991 | null | 1.028874 | 2.376895 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:13:19 | 2953077582855141137037916496044547450023108732216889192188591287994737960844894885678411215229565216015451468206697181732609635214458049204431197161163442 | PO_2953077582855141137037916 | {"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, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 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], "sub-config": "spc_rs_anti", "hash": "13383993830272605086388361181036213913836593156340899682449997787345260753475138808026642762789139523677928004076965232067495120884298268355388559603176711", "id": "MD_1338399383027260508638836"} | MD_1338399383027260508638836 | null | [
"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 | 2165761157767063061443948114425606836908258478364346280248929385325236311067706055715272964279973761663245793091019826120176716983163005285656284470384370 | CO_2165761157767063061443948 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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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| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 2213476760832523271135110496025865490458767433329659985865362225104891484970969073869304574178654266551478306166914113495075500182019174078006924614048566 | CO_2213476760832523271135110 | GST_GAP_22_main | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 2278648374754056084490207632153163799584899742972390835221917522996607706766960428862606153246421807693335188419765852039983315823186398883432609492238055 | CO_2278648374754056084490207 | GST_GAP_22_main | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| [
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| 2023-12-05T20:21:09 | 2286284522507695914688449037489216029453989989092934747840221907435071061957412890386399017360799570470668569695396976229621952720696869751269489022361447 | CO_2286284522507695914688449 | GST_GAP_22_main | [
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"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
GeTe | GeTe | AB | [
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| null | null | null | -3,094.230436 | null | 48.82643 | 48.82643 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:38:22 | 4901244925378873948022122636509217392223453005715478161464967806040157705988362484756921296185754070635626736600433482470861120893058158181321090924251583 | PO_4901244925378873948022122 | {"config-type": "dimer", "masses": [72.63, 127.6], "sub-config": "dimer", "hash": "807594828288789545437278021553428837212676628101887119530568882245764563713766611640257142995090963270193165946211510593315120100525733532760053725978816", "id": "MD_8075948282887895454372780"} | MD_8075948282887895454372780 | null | [
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| [
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| 2023-12-05T20:21:03 | 2338366286023303330349857067337970163090945545455443609181780455282167174397111018518036458323266534788357608653553321376900710603151591102836968672700001 | CO_2338366286023303330349857 | GST_GAP_22_main | [
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"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Te3 | Te | A | [
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52
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| [
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| true | null | null | -1,706.052479 | null | 0.735579 | 0.984208 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:31:12 | 6731981179401242483878160316604492730963581582848912903378796977905915080339645473535770202589199348709363977680352428687521072393956796136485691439713234 | PO_6731981179401242483878160 | {"config-type": "cryst", "masses": [127.6, 127.6, 127.6], "sub-config": "conv_ele_cryst", "hash": "12686753169410293216915183305074981836119636246803503793562969877295476398725106144809830780309443770147449043505381746569096111203910703982470812304423691", "id": "MD_1268675316941029321691518"} | MD_1268675316941029321691518 | null | [
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"extended_gst_gap_22_for_efield_pbesol_cryst_conv_ele_cryst",
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| [
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| 2023-12-05T20:21:04 | 2378452186666079910683990929682313521873331039305253400124476691067052215145072435616898802642162034605175311869244563633744712245636120641025102193186512 | CO_2378452186666079910683990 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
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1
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| [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Ge9Sb6Te18 | Ge3Sb2Te6 | A6B3C2 | [
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| true | null | null | -47,370.363517 | null | 0.167163 | 0.3563 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:54:51 | 3597639334192284153655977588771329143626678194323197697304927395127421641392620958859709664865893201878683207767553570208525513761128836927654865567801463 | PO_3597639334192284153655977 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "conv_hex", "hash": "3671207608404505370682589606096776184028544354012001044982517601694972033807052280135612196966234880039393893029496696662628098506552426897096802559474888", "id": "MD_3671207608404505370682589"} | MD_3671207608404505370682589 | null | [
"main_gst_gap_22_pbesol_cryst_conv_hex",
"extended_gst_gap_22_for_efield_pbesol_cryst_conv_hex",
"refitted_gst_gap_22_pbe_cryst_conv_hex"
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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:05 | 2394511196596626199779180721408366876038822147808330215698087542002815349155539003490993775727031325144066459951209311809978860799657456599050993110331991 | CO_2394511196596626199779180 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_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"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:03 | 2402637663898465415358691043916420534470407554098464291786763766668090073990911715029209546991121710508355915354908318415396657610412387206890212009919544 | CO_2402637663898465415358691 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
GeTe | GeTe | AB | [
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52
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| The main training dataset for GST_GAP_22, calculated using the PBEsol 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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Te3 | Te | A | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 main training dataset for GST_GAP_22, calculated using the PBEsol 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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| true | null | null | -82,622.914468 | null | 1.108424 | 6.027581 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:14:16 | 215799240083741613485771252068654134131640367330601634666166317971834969861762783198775276139343266580280614000022504122128023675865554867410741416231632 | PO_2157992400837416134857712 | {"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, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "conv_rs_anti", "hash": "5073117287515818233141285278410823651751301141196327268537929970241044359457776115326502921231676853878585816727795291763981466751934393845657244040793901", "id": "MD_5073117287515818233141285"} | MD_5073117287515818233141285 | 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"
]
| 2023-12-05T20:21:05 | 2501719192461998444953605378138813304112830643216572626397356637360509736123563284345630687863692439484025236741887246596467250695501464429194548583459265 | CO_2501719192461998444953605 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
3
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| 2023 | [
0.23630478379677952,
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 main training dataset for GST_GAP_22, calculated using the PBEsol 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_iter_1_standard_iter",
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| [
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"DS_jy3ylaf48xg3_0",
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| 2023-12-05T20:21:04 | 2518218495692476398256880872914574863295758473591489891812362198811748822955104008706079550516029302299253058146913607238537058292328057391975719925927996 | CO_2518218495692476398256880 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
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| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| true | null | null | -256,777.811621 | null | 0.485068 | 1.228624 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:28:32 | 1047509912986602790052697340687697143909819919583626098915042650752870117962773049426965037527526676484971565338107251590705403364977841155428058359636974 | PO_1047509912986602790052697 | {"config-type": "aimd", "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": "disorder", "hash": "635248437604434067609015623060890549352218345879182734697206382522581778010191842151436918960043307897262912166286477599899010648944335502521095346736784", "id": "MD_6352484376044340676090156"} | MD_6352484376044340676090156 | null | [
"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 2536281333225841227020609476993491918533667631039047283019006547820899770358225401999483671535531938320668503276460044873931630045142306365237116692126545 | CO_2536281333225841227020609 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
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1
]
]
| [
3
]
| 2023 | [
0.23630478379677952,
0.21855465772221375,
0.5451405584810067
]
| 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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Ge54Sb36Te108 | Ge3Sb2Te6 | A6B3C2 | [
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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 | 2545732987247167403561839502934589348111960926286628877843683922910496264307547505340060569204823670599447338281426330443883666292889184832469499653933907 | CO_2545732987247167403561839 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
3
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| 2023 | [
0.23630478379677952,
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| true | null | null | -327,992.201906 | null | 0.776204 | 2.276112 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:48:35 | 1250236467455655311557589651126120908506274429748252096021549775543628054312570503646594904309000348109586052701124234962704646035172139266760966899993498 | PO_1250236467455655311557589 | {"config-type": "iter_6", "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, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 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": "172155259265418825957308399417456085803562192800116323034729402882622337253304510112122050199694671908953791477386540562761281764995534361919712824229771", "id": "MD_1721552592654188259573083"} | MD_1721552592654188259573083 | null | [
"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:09 | 2579017699923545946736713864814580937904801654815546784167534630407007952751793700733206645513716895841174915875811875964602980709234661482112887095548803 | CO_2579017699923545946736713 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.23630478379677952,
0.21855465772221375,
0.5451405584810067
]
| 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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| [
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| [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol 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_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 | 26997024220475381040913402894436864977337870513356068246479745862929480724365065248459173797773856056145857542671953418291872723164480156991969233643572 | CO_2699702422047538104091340 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 2779808345506524930316751606757843502158067774142181171223060228624778628534597548648288774167969111429244254911134932764468803439395052355206589638246446 | CO_2779808345506524930316751 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
0.23630478379677952,
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_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"
]
| 2023-12-05T20:21:09 | 2788440834985530190798516664382120478256801939138457567886000528044521782817301713403359793571212645912666618773020611913819747211536010799123611903354761 | CO_2788440834985530190798516 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_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"
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| 2023-12-05T20:21:04 | 279864102820960616363890775440270224588578269012021589469733663307335763041984814329776708751525149689981108282227711481541447691360500157650704730019013 | CO_2798641028209606163638907 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"main_gst_gap_22_pbesol_iter_3_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_3_standard_iter",
"refitted_gst_gap_22_pbe_iter_3_standard_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 2801417566738419124319845355106966299374772545320107916171981565967275239754283576813707800933773563287661436345849399065667958802698522397752603008847876 | CO_2801417566738419124319845 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| [
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| 2023-12-05T20:21:04 | 2872841335046081807640400285730039993159928799332425895771712961743364046001365531709335915882315058752367085444593285287198082115923105298915402514917725 | CO_2872841335046081807640400 | GST_GAP_22_main | [
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"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| true | null | null | -268,153.607092 | null | 0.573709 | 1.622531 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:09:25 | 1509136416601200811325795384979502235143566093950112838953462018084398960908805072100423814412398669119569860986503074014343546674521009995416156228380890 | PO_1509136416601200811325795 | {"config-type": "iter_2", "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, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 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": "6679672251013649887665489667459377954998629168511967167813625165315706455779316658389822703028940588146827511325193916810202420825768562445387728573125585", "id": "MD_6679672251013649887665489"} | MD_6679672251013649887665489 | 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 | 2875181588742918089581473600563802928853655669267970663548390627011197015422628806465999664671609262436230454669227391850611944470540795401294878139030169 | CO_2875181588742918089581473 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
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1
]
]
| [
3
]
| 2023 | [
0.23630478379677952,
0.21855465772221375,
0.5451405584810067
]
| 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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 main training dataset for GST_GAP_22, calculated using the PBEsol 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 main training dataset for GST_GAP_22, calculated using the PBEsol 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 main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| true | null | null | -258,562.447071 | null | 0.632475 | 2.27256 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:30:25 | 1719308652409666844618478045528669971536901389148777475145929085667836611027598688607270383459685106041755631558357904649494478516264679064717756733554812 | PO_1719308652409666844618478 | {"config-type": "aimd", "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, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 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], "sub-config": "disorder", "hash": "8033838607892980577709491580046211376395630924963306477645608199876392881078949947912973252060998933131721714600025174849847750083144652361714845799364030", "id": "MD_8033838607892980577709491"} | MD_8033838607892980577709491 | null | [
"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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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:03 | 3065662385145387294629507116432591318888644902379277825929872708336553296864672505586494789056166502856246619051241308278385986257785017287853527493907489 | CO_3065662385145387294629507 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
0.23630478379677952,
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"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 | 3093505670231858061075935430255422611425641838720287188173293340175900867730679981370119201028914531001754246683113877638976743036969533567964791450806747 | CO_3093505670231858061075935 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| true | null | null | -64,200.108336 | null | 0.584125 | 1.41036 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:19:32 | 3946459560889361392185936959241890802410409583683216684445089940602616943785748432215729555022112392037178231985740813353153158804108392856029633664112897 | PO_3946459560889361392185936 | {"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, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 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": "4258300117514373197389689128291023977616299557097794243333972937154259204258235931656677942732644191457793616264176506159502295554843995743447397856776532", "id": "MD_4258300117514373197389689"} | MD_4258300117514373197389689 | 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"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 3093534365532735785671025853323034423925221634863340412401982323317824901981142323220342905627123966364323566750669787613377918824402516269265363637895170 | CO_3093534365532735785671025 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
3
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| 2023 | [
0.23630478379677952,
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Ge28Sb56Te112 | GeSb2Te4 | A4B2C | [
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| 2023-12-05T20:21:05 | 3110967429327253190968144039513701728296410512864484549064164284613030138773076934004367552419848067682452949998575018961379407926876737556218751669126910 | CO_3110967429327253190968144 | GST_GAP_22_main | [
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"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
GeTe | GeTe | AB | [
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| true | null | null | -3,106.402525 | null | 0.240949 | 0.240949 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:45:25 | 9913766087239129751678960188526293843190088484438518838850604277526899872087746940855775920351733908722776708391049240722264686515251300394900469296928212 | PO_9913766087239129751678960 | {"config-type": "cryst", "masses": [72.63, 127.6], "sub-config": "rhom_GeTe", "hash": "6240288368831179278655173368742685790632375324962132005821718706446864372084663405911025033538700741186128949108475768232718859484959809394658838888233988", "id": "MD_6240288368831179278655173"} | MD_6240288368831179278655173 | null | [
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"extended_gst_gap_22_for_efield_pbesol_cryst_rhom_GeTe",
"refitted_gst_gap_22_pbe_cryst_rhom_GeTe"
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| [
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"DS_jy3ylaf48xg3_0",
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]
| 2023-12-05T20:21:03 | 3112911949151434095868269007333795533179345589683117522087252450921591923789314346411287235879837148175730440115531059529264883682768914019507212860682328 | CO_3112911949151434095868269 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
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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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| 2023-12-05T20:21:06 | 3137969430183728417097173298477300123949048595865482468510082787693658634336429347257750892586852231507021821447714576506759015102633277191235616527460913 | CO_3137969430183728417097173 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| true | null | null | -245,898.202203 | null | 0.656479 | 1.83267 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:32:46 | 3285904263127272025987643465612370659216457281432132040173403592035716127428987423553107751996499095578225127886295478367582797501088204521939418238006943 | PO_3285904263127272025987643 | {"config-type": "iter_1", "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": "1959771366508673057861790382182001457631683302676572976558529536525964596125084971701984267848781356181276210639832177160602926926264695099721314545521638", "id": "MD_1959771366508673057861790"} | MD_1959771366508673057861790 | null | [
"main_gst_gap_22_pbesol_iter_1_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_1_standard_iter",
"refitted_gst_gap_22_pbe_iter_1_standard_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 3141270045156715766979174602016029448071529876367811731548687106313367980131077298831600493401330112231452403208844570291159186860833374043916811251911751 | CO_3141270045156715766979174 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
3
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| 2023 | [
0.23630478379677952,
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Ge36Sb24Te72 | Ge3Sb2Te6 | A6B3C2 | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| true | null | null | -30,709.163867 | null | 1.154445 | 2.341838 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:29:58 | 8026178986572635150464401056889629494518760967757483846844263760615055242355020068170321620337189407904506333510040127233034276975681592061498987112428377 | PO_8026178986572635150464401 | {"config-type": "cryst", "masses": [127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "spc_ele_cryst", "hash": "10482341115092695104624036464431580998332388409744383351399034763727849966709074052587122925035349033589465704752437318101099927297656895760288257555499527", "id": "MD_1048234111509269510462403"} | MD_1048234111509269510462403 | null | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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 | 3198993288628641425221495167420315827679178565705930605526222973672056650278097688527606035416365516956072379350722153579336423962144385959811497629164044 | CO_3198993288628641425221495 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
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| 2023 | [
0.23630478379677952,
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_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 | 3200417117149002372140506906768343167272367644709848086500327792323379031466969466578250383167006152903099683223684462965248497249478743623560677745450835 | CO_3200417117149002372140506 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
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| 2023 | [
0.23630478379677952,
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_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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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 3246608924955815407373197658716440475807179740015159785013754783707201295208933485822166585302765331165130824662514906023166660642828186464632310599064478 | CO_3246608924955815407373197 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
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| 2023 | [
0.23630478379677952,
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_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"
]
| 2023-12-05T20:21:08 | 3256590940276117951898189280717376432478369195686680542505284042470710704339806133671909494013418839664929274978646914293324272592645210711652326678245791 | CO_3256590940276117951898189 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
0.23630478379677952,
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_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 | 3261048517465119956176500820455068750466976380084378892829979013030406677168044153644502304598079201302305983650666046397746837527629440661814613128727970 | CO_3261048517465119956176500 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
3
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| 2023 | [
0.23630478379677952,
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_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 | 3263982200038663979501033086315345324330489618146644732417079024880742031775056228300721433169193838744797281060530712752569974977686523591528389017036462 | CO_3263982200038663979501033 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
3
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| 2023 | [
0.23630478379677952,
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_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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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:08 | 3277312738832514086211541261874542774425190333973201401350594164986983512571433735380546667647562373854544825085874960496641444657617633813808695825595213 | CO_3277312738832514086211541 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
0.23630478379677952,
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_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 | 3280639421268873888658509091911932502651356360006650289356185664594343743454368988294214704265327500890608187349523177535354509129534849887090469253884525 | CO_3280639421268873888658509 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_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 | 328780449440117419787619106896361417055011432884510042496458130732080081312361184515897292461158109407176104922061186374260225384272462295759627493459201 | CO_3287804494401174197876191 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
0.23630478379677952,
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_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 | 3297551326946722751703928429939086322410832985983443808369222165927987293839190604954421748344375080798055629341580138378787424014963684886646903018401433 | CO_3297551326946722751703928 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_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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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 3325867745476556826747688172056170935515540945387746613325086378732961083979629645870659738544874230397183546208178603666719731921218419952541293997146748 | CO_3325867745476556826747688 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| The main training dataset for GST_GAP_22, calculated using the PBEsol 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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| 2023-12-05T20:21:07 | 3362966051418118736818951166873315243202650012833006823654270682088216350639023967448163814353558212307237142566464195342861405728575367750341122344914538 | CO_3362966051418118736818951 | GST_GAP_22_main | [
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"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| true | null | null | -30,706.440345 | null | 0.944164 | 4.229248 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:14:18 | 585338195023787749910257344750208220784966285301591807330806101595960410187023145245619591289042617795638503052498965561262152676024252513640485383778995 | PO_5853381950237877499102573 | {"config-type": "cryst", "masses": [127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "spc_ele_cryst", "hash": "10482341115092695104624036464431580998332388409744383351399034763727849966709074052587122925035349033589465704752437318101099927297656895760288257555499527", "id": "MD_1048234111509269510462403"} | MD_1048234111509269510462403 | null | [
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| [
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| 2023-12-05T20:21:04 | 3363972476257137466077244971371854067702361548930222535087779103871493303460076169790450253609523236870331776498841511316302077365103478735552339284213888 | CO_3363972476257137466077244 | GST_GAP_22_main | [
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"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| true | null | null | -284,176.476807 | null | 0.626748 | 2.429088 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:13:33 | 5594459975805821421339370562422332464268400236151780565312441872976814685201796107727227526675102176993326990589385144201971641667863764050136898854304802 | PO_5594459975805821421339370 | {"config-type": "aimd", "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, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 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], "sub-config": "disorder", "hash": "11598964150824304561406563066599627773802852226592167872332117863146893682976906357645543298602203713007596255418592805153267519841194089810872727949878097", "id": "MD_1159896415082430456140656"} | MD_1159896415082430456140656 | null | [
"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 3395613080492232399614054753937435163100790205433245706134769031023664064831457873030491861517132367849241756341529832395924932108890797877526385736053143 | CO_3395613080492232399614054 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol 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 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.23630478379677952,
0.21855465772221375,
0.5451405584810067
]
| 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/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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