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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Te3 | Te | A | [
52,
52,
52
]
| [
"Te"
]
| [
1
]
| 1 | 3 | [
[
4.806802,
0,
0
],
[
-2.285936,
4.031132,
0
],
[
0,
0,
6.428981
]
]
| [
[
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[
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[
2.00160104,
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-0.09522607
]
]
| [
true,
true,
true
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| [
1,
1,
1
]
| 3 | 5454617883434966367851702620672625734151144763710520263410452041936095169508985481618185968677838601987745367530093158645501969376891505309189414654352939 | 1 | CASTEP | PBE | null | [
[
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],
[
0.26486,
0.22447,
-0.89077
],
[
0.24478,
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0.72661
]
]
| null | [
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[
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],
[
0.0015248169337050757,
-0.01039102527509567,
-0.023701761599857814
]
]
| true | null | null | -1,715.677655 | null | 0.768047 | 0.956038 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:58:24 | 10046621230725875058934631265141580691542850303584760483455531605416301211322059960193861759868194851804379964947731963216650380545018766675987689921983078 | PO_1004662123072587505893463 | {"config-type": "cryst", "masses": [127.6, 127.6, 127.6], "sub-config": "conv_ele_cryst", "hash": "12686753169410293216915183305074981836119636246803503793562969877295476398725106144809830780309443770147449043505381746569096111203910703982470812304423691", "id": "MD_1268675316941029321691518"} | MD_1268675316941029321691518 | null | [
"main_gst_gap_22_pbesol_cryst_conv_ele_cryst",
"extended_gst_gap_22_for_efield_pbesol_cryst_conv_ele_cryst",
"refitted_gst_gap_22_pbe_cryst_conv_ele_cryst"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 10018405471335832097079645280886436212155154772636793466114786718260239086635474493934768665809411155715213228196100096802403966083958821452552152121680620 | CO_1001840547133583209707964 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.23625529319304173,
0.21859567629705223,
0.545149030509906
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Ge2Sb2Te5 | Ge2Sb2Te5 | A5B2C2 | [
32,
32,
51,
51,
52,
52,
52,
52,
52
]
| [
"Ge",
"Sb",
"Te"
]
| [
0.2222222222222222,
0.2222222222222222,
0.5555555555555556
]
| 3 | 9 | [
[
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0,
0
],
[
-2.124405,
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0
],
[
0,
0,
17.958756
]
]
| [
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],
[
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3.70320324
]
]
| [
true,
true,
true
]
| [
1,
1,
1
]
| 3 | 9796516303732611655892859553958586329575098563419861485209169141337188961862949794634948657241470752994651941431511737848019241594433516791520781232986263 | 1 | CASTEP | PBE | null | [
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[
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[
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],
[
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]
]
| null | [
[
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[
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]
| true | null | null | -12,706.118309 | null | 1.003041 | 1.43579 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:17:01 | 9270152479050855035216909431216190454830734715750057934237637623783842579724370126609997544854524682558475689972845376839699220526919309801403351248474626 | PO_9270152479050855035216909 | {"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 | [
"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"
]
| 2023-12-05T20:21:04 | 10035896026621352751751988518305793083161023785746438568879427630500151531155696037428610339326619811880042295416644290352763264863628364761096196069309261 | CO_1003589602662135275175198 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.23625529319304173,
0.21859567629705223,
0.545149030509906
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Ge8Sb16Te32 | GeSb2Te4 | A4B2C | [
32,
32,
32,
32,
32,
32,
32,
32,
51,
51,
51,
51,
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]
| [
"Ge",
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]
| [
0.14285714285714285,
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0.5714285714285714
]
| 3 | 56 | [
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0,
0
],
[
0,
12,
0
],
[
0,
0,
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]
| [
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| [
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true,
true
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| [
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| 3 | 8177996294848052382041409818609649450555716215577650449585270712637450750792402331939146000327287806561298363518886548454684431353330722395554262365341692 | 1 | CASTEP | PBE | null | [
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| true | null | null | -76,760.772821 | null | 1.423813 | 4.140578 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:20:08 | 6849993613949762168510971820038740830979008160433668092897883695546322173181522586371542012415853342114181080934657384014589911285053667366042293838681383 | PO_6849993613949762168510971 | {"config-type": "cryst", "masses": [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, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "conv_rs", "hash": "3558150408544697676330891047416421949637381360072465706128507139136048775706449967444597095304685706452033314150590934437672173605413493948631421337772505", "id": "MD_3558150408544697676330891"} | MD_3558150408544697676330891 | null | [
"main_gst_gap_22_pbesol_cryst_conv_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_conv_rs",
"refitted_gst_gap_22_pbe_cryst_conv_rs"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:05 | 10047938266282435449555545971732008211033823452388325953470257285002410787892151985522144357430726465934566587767934153007840314494159192561725136149719992 | CO_1004793826628243544955554 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"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 | 10100083533594569062195700343360612609520918492973161650363649698682394013550206725818818677031545249115496835772741086203847447627865697010261269629171060 | CO_1010008353359456906219570 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
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]
]
| [
3
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| 2023 | [
0.23625529319304173,
0.21859567629705223,
0.545149030509906
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| 2023-12-05T20:21:05 | 10110071186649266351072495873822708907507919054818332025017182640799323168060602397666473801902982757477638372187487742335008710406161890752168889358405053 | CO_1011007118664926635107249 | GST_GAP_22_refitted | [
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| 2023-12-05T20:21:03 | 10146059375986250504870488571345713926049647549889030623508600863668808337711466258243716308022788005656793174691496117989148452793569707637137242566995373 | CO_1014605937598625050487048 | GST_GAP_22_refitted | [
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"Evan Ma",
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]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -162,433.711805 | null | 2.905183 | 14.288298 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:25:31 | 4765164433846461311572745866604520527117799874707497770340500166466253433274958927763005146313840595424263656194169992908472868901103117355981070315428176 | PO_4765164433846461311572745 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63], "sub-config": "spc_ele_cryst", "hash": "1406383406495818635985055412089368180179295071351469529322162521485023015689134506503088551415028905432151238287189538657629899480462676899181405964420533", "id": "MD_1406383406495818635985055"} | MD_1406383406495818635985055 | null | [
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"refitted_gst_gap_22_pbe_cryst_spc_ele_cryst"
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| [
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]
| 2023-12-05T20:21:04 | 101537650837000911854255181027913503487206903333158605130023519031972075288512693023219279466572162129560580444903411190156599088856509316708036504244445 | CO_1015376508370009118542551 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
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1
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| [
3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
GeTe | GeTe | AB | [
32,
52
]
| [
"Ge",
"Te"
]
| [
0.5,
0.5
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| 2 | 2 | [
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| 3 | 9951339218287180281068971818565158647516043524733261274196284280348995595708863357301007812936275989750453392211961930363028597860390628862448432284753417 | 1 | CASTEP | PBE | null | [
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| true | null | null | -3,110.616245 | null | 0.179054 | 0.179054 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:42:38 | 6903376477649016135922502471836867134201166191799605461512637077270240728758330574999141055951905443883935460079727643100739799787726538206781583110524197 | PO_6903376477649016135922502 | {"config-type": "cryst", "masses": [72.63, 127.6], "sub-config": "rhom_GeTe", "hash": "6240288368831179278655173368742685790632375324962132005821718706446864372084663405911025033538700741186128949108475768232718859484959809394658838888233988", "id": "MD_6240288368831179278655173"} | MD_6240288368831179278655173 | null | [
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"refitted_gst_gap_22_pbe_cryst_rhom_GeTe"
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| [
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]
| 2023-12-05T20:21:03 | 10204876069420980467855565691681991781715666210088676323294637203763493820004485910588049395566472589309681872568382320199498063678050754923474685899424384 | CO_1020487606942098046785556 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
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1,
1
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]
| [
3
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Sb3Te3 | SbTe | AB | [
51,
51,
51,
52,
52,
52
]
| [
"Sb",
"Te"
]
| [
0.5,
0.5
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[
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| 3 | 9250810571714500645154571616983063376019790358009068330296126137416537147328323972656188346654940354530959536355206473777129430800579742781849503233689008 | 1 | CASTEP | PBE | null | [
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| true | null | null | -8,869.745472 | null | 0.856056 | 1.380415 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:21:48 | 9720304271944448862668273907231554153083274818704497455828254859574980439167706287159255481789894546413734980980761830720643442305923018581346422063202567 | PO_9720304271944448862668273 | {"config-type": "cryst", "masses": [121.76, 121.76, 121.76, 127.6, 127.6, 127.6], "sub-config": "MP", "hash": "10217346206216549225131140678436504793353355317643081899948485688000389515285471674369097264338264963227107133936950654200823616156628129723923415110778397", "id": "MD_1021734620621654922513114"} | MD_1021734620621654922513114 | null | [
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"extended_gst_gap_22_for_efield_pbesol_cryst_MP",
"refitted_gst_gap_22_pbe_cryst_MP"
]
| [
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| 2023-12-05T20:21:06 | 10225848408455801455663193352431252026438188672267126751987193956444566960791769565597472948172597315246174358039399733996248957760870470389081590830168699 | CO_1022584840845580145566319 | GST_GAP_22_refitted | [
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"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
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"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -14,305.780831 | null | 3.762374 | 9.294802 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:17:18 | 7969895386100999986939054284772119514660665418927208744275306487430647097713707406594710534046775375155421346990966308020261780633164074226234975549250825 | PO_7969895386100999986939054 | {"config-type": "cryst", "masses": [121.76, 121.76, 121.76, 121.76, 121.76, 121.76], "sub-config": "conv_ele_cryst", "hash": "11376588209332849124881644127347225818768470654784049131706689891424440782678255600298876432210759186988575066593177855023956768212279145568357977314864761", "id": "MD_1137658820933284912488164"} | MD_1137658820933284912488164 | null | [
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| [
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| 2023-12-05T20:21:04 | 10269890556828362828037472297246232409528996382037649389642563441413959623516865489237856898315491699062943936245438643762278118441869419691992120204042965 | CO_1026989055682836282803747 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
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"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -4,762.575653 | null | 1.32371 | 1.32371 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:17:58 | 6957381117597258536270951306298666719742577145836676301590152535952773858325305223363724997077648786349865496216375530672853404075743012485250533031632106 | PO_6957381117597258536270951 | {"config-type": "dimer", "masses": [121.76, 121.76], "sub-config": "dimer", "hash": "12749784377554201583291612343634491581939460251612151514457680212018420488370028909551115401499445089420899938851942171239271722616911573405893787570700809", "id": "MD_1274978437755420158329161"} | MD_1274978437755420158329161 | null | [
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| [
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| 2023-12-05T20:21:03 | 10296888355917090295799467997972686654688465637758967014930785032532147029116569293805519362716796740577631528469038593172109289989717514360321246481500024 | CO_1029688835591709029579946 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 10334093914762868737471281516692517428664198550544377143139590518464382138908081656620058043555599015738120141671736783994927937183842146850757385693777197 | CO_1033409391476286873747128 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.23625529319304173,
0.21859567629705223,
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]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Ge36Sb72Te144 | GeSb2Te4 | A4B2C | [
32,
32,
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| true | null | null | -345,425.252004 | null | 0.72211 | 2.100039 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:06:39 | 8708681719952468971449076735974564383676596853208260889126031308540721933558982930142272102996326385477751300650744517297328868765255338219806494436616451 | PO_8708681719952468971449076 | {"config-type": "iter_4", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 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": "8118140186940381672954095266362896558390339254427388054420619789863040712380064009709598223325603583448397929143707463844011379879958469791991181746045266", "id": "MD_8118140186940381672954095"} | MD_8118140186940381672954095 | null | [
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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 10337169375869713170523168879758583748319966620828864638719957503103543796327320033510362235771393016611848358869133464519538205600357389007885838418187183 | CO_1033716937586971317052316 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
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| [
3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
GeTe | GeTe | AB | [
32,
52
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| [
"Ge",
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| [
0.5,
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| 3 | 8488416458573019617348400926803191566295299193559063164425722664376715372554144213137062483818405326301091814583741723495049269030244840211878609222101028 | 1 | CASTEP | PBE | null | [
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[
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| true | null | null | -3,110.605193 | null | 0.269234 | 0.269234 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:47:16 | 5520451606464985177990908735168461996306793360449192934846554760314421703740922554072636588415026032730807108493682877694805766496361402636989390261066947 | PO_5520451606464985177990908 | {"config-type": "cryst", "masses": [72.63, 127.6], "sub-config": "rhom_GeTe", "hash": "6240288368831179278655173368742685790632375324962132005821718706446864372084663405911025033538700741186128949108475768232718859484959809394658838888233988", "id": "MD_6240288368831179278655173"} | MD_6240288368831179278655173 | null | [
"main_gst_gap_22_pbesol_cryst_rhom_GeTe",
"extended_gst_gap_22_for_efield_pbesol_cryst_rhom_GeTe",
"refitted_gst_gap_22_pbe_cryst_rhom_GeTe"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:03 | 10390071811891115581668609154679258695482176618433170737360367182720953871158580933147503063108281679346625554903630556258055429459448947473075353866449342 | CO_1039007181189111558166860 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
1,
1
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| [
3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Sb2 | Sb | A | [
51,
51
]
| [
"Sb"
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| [
1
]
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[
20,
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0
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[
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[
0,
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| [
[
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true,
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| [
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| 3 | 8404190502740663714654830936460192160435274799801588678543494289689155845829422562339809592938503056327580905176575986491937633506463261704308636226887561 | 1 | CASTEP | PBE | null | [
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| true | null | null | -4,763.72766 | null | 2.41274 | 2.41274 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:56:37 | 11056028453937507812295088770396389876607610564885161261032247698249162700836387690164100225450883882690628243121616499612431046145063394233690567590561698 | PO_1105602845393750781229508 | {"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 | 10430315847012841309093566660074950098073255858204758702408437089541240843703845407969766947976624971787578567739706129660847154924032272318070243261093641 | CO_1043031584701284130909356 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
1,
1
]
]
| [
3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Sb6 | Sb | A | [
51,
51,
51,
51,
51,
51
]
| [
"Sb"
]
| [
1
]
| 1 | 6 | [
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[
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0
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[
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[
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true
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| 3 | 9719026490698724748504087828129631533176843270217326398991280179110735096130591680662382441831913714745444281791979711235568167319905789462304806735718591 | 1 | CASTEP | PBE | null | [
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| null | [
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| true | null | null | -14,307.34289 | null | 1.856532 | 2.859652 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:19:28 | 9953998206460206372800682549754909875044921528571032453864427993116287272033574469970362480638149888958756405672158150984844495421169762038482751822895574 | PO_9953998206460206372800682 | {"config-type": "cryst", "masses": [121.76, 121.76, 121.76, 121.76, 121.76, 121.76], "sub-config": "MP", "hash": "9069567669392064714873104418491718386849704648862022330747184259839218735894581374409365097957519485649702557015669074897850515870839152374034858966578730", "id": "MD_9069567669392064714873104"} | MD_9069567669392064714873104 | null | [
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"extended_gst_gap_22_for_efield_pbesol_cryst_MP",
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]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 10528199607094269224101084392103345108111909514702398241631405145812815121222849656565220630124777765204731791063894401375532172195319113635074548216885180 | CO_1052819960709426922410108 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.23625529319304173,
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]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Sb6 | Sb | A | [
51,
51,
51,
51,
51,
51
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| [
"Sb"
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| [
1
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| [
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 10740886016099042104159133199715655989436779774128084975497185829493275118763549789170941181727076850019541412599811790166254368566187144098634852439513703 | CO_1074088601609904210415913 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
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| true | null | null | -30,885.083786 | null | 0.833158 | 0.83404 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:34:01 | 3777949737437354504649808497982844257599379908976549941431851065171620378157969026389470530077743941499884795140691510190544718918857256894884181673689311 | PO_3777949737437354504649808 | {"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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"extended_gst_gap_22_for_efield_pbesol_cryst_spc_ele_cryst",
"refitted_gst_gap_22_pbe_cryst_spc_ele_cryst"
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| [
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"DS_jy3ylaf48xg3_0",
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| 2023-12-05T20:21:04 | 10759764952820913095960083581629214787130425062604943091536432366602450636967301236256280884809018337753315695297141327896062557851342096240194669611367552 | CO_1075976495282091309596008 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -114,460.56119 | null | 1.723784 | 6.601156 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:22:51 | 7576379716930170049925230749291457225270890111486294462636253485086920040574575357799218902564811138235737454927387227920741904387563805883839124247235665 | PO_7576379716930170049925230 | {"config-type": "cryst", "masses": [121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76], "sub-config": "spc_ele_cryst", "hash": "2715333019700714844145994706090996120496929084575008822287006054578880522607264016889143281893607225757542324686604781612875117960083345583094603658552292", "id": "MD_2715333019700714844145994"} | MD_2715333019700714844145994 | null | [
"main_gst_gap_22_pbesol_cryst_spc_ele_cryst",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_ele_cryst",
"refitted_gst_gap_22_pbe_cryst_spc_ele_cryst"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 10797243198631632031475152684677905494282460035131203072377114377977435074791994410104091494756957462215257941118330994600454262873492952026801835515209165 | CO_1079724319863163203147515 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
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| [
3
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
GeSb | GeSb | AB | [
32,
51
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| [
"Ge",
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| [
0.5,
0.5
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| 3 | 1059833785926084613516846973738089027157053014016101231940372888567629621519160434910367350132131211290655895280265874856423443594789891046715787982916470 | 1 | CASTEP | PBE | null | [
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| true | null | null | -4,916.340269 | null | 1.64986 | 1.64986 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:36:27 | 9661998742752553681227844238299780305169680127056087514578724515192086015588253024510613535842042945048449078331695925748948200493423274477623307760143578 | PO_9661998742752553681227844 | {"config-type": "dimer", "masses": [72.63, 121.76], "sub-config": "dimer", "hash": "2476909026557004540199065918619711435370016570595460498664850021951465726236656764379458641489718363378303496598795948492948614158804944841913182154056778", "id": "MD_2476909026557004540199065"} | MD_2476909026557004540199065 | null | [
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"extended_gst_gap_22_for_efield_pbesol_dimer",
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]
| [
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"DS_jy3ylaf48xg3_0",
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]
| 2023-12-05T20:21:03 | 10809169628439758009007099203476797871407601725687162284158009980176376523349892604792400444365155676518293736941490013776001330216508416356732760401433819 | CO_1080916962843975800900709 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
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| [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Ge8Te8 | GeTe | AB | [
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| [
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| [
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| 2 | 16 | [
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| 3 | 2126761836381036652300691257180458792841055995551152455157553471395367785537020118374945851001280280069200983767378522324848503482283632573169791304740487 | 1 | CASTEP | PBE | null | [
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| null | [
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| true | null | null | -24,883.340014 | null | 0.071898 | 0.088135 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:18:24 | 1472769263573584034128452413860803192479839041839986185207326162817010215639073907381050205040699415826545935137931397186986354398000438325843327057996205 | PO_1472769263573584034128452 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "MP", "hash": "1180107191253180060243643854025583774818101685576481309165808484133148277250249339393265346190729021137405568446684714649059769373637553040112317803611680", "id": "MD_1180107191253180060243643"} | MD_1180107191253180060243643 | null | [
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"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 | 10832033457343489660959599495953137656883870751405033208176373694753913198730503656840321145679098967756895205938046420603052280028850437685991719394892134 | CO_1083203345734348966095959 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.23625529319304173,
0.21859567629705223,
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]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
GeTe | GeTe | AB | [
32,
52
]
| [
"Ge",
"Te"
]
| [
0.5,
0.5
]
| 2 | 2 | [
[
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0
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[
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0
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[
2.14,
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| [
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| [
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| 3 | 8666859956202815081393194958836005488259296209532949444609964441584348496760894592585323012008160770398116333213681057448053934687630125138165217332442847 | 1 | CASTEP | PBE | null | [
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[
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| null | [
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| true | null | null | -3,110.600799 | null | 0.134453 | 0.134453 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:44:22 | 4568827543792670966013856073768201909775973828156606069521599266031539044259454983522275482403082874997236272427370470296298637705043411783412770267810228 | PO_4568827543792670966013856 | {"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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"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:03 | 10916690930500130008285688378828766006972103386896009845128237878704682744862369810222154867094147640633088989287683234098165782284054654863160965702141456 | CO_1091669093050013000828568 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.23625529319304173,
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]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Ge28Sb56Te112 | GeSb2Te4 | A4B2C | [
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| [
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"DS_jy3ylaf48xg3_0",
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]
| 2023-12-05T20:21:06 | 109623739753271807423392146850764123805237208756250830854793023409943213007518007693672619625753562566483971567980528147325982557946458588183013529373509 | CO_1096237397532718074233921 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
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| [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
GeTe | GeTe | AB | [
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52
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| [
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| true | null | null | -3,110.603409 | null | 0.430856 | 0.430856 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:24:20 | 727042499572552105120041644612671001460687176225637029755321268083516201515849753051749110763896413547318805684843683872535556874467264022855652977905674 | PO_7270424995725521051200416 | {"config-type": "cryst", "masses": [72.63, 127.6], "sub-config": "rhom_GeTe", "hash": "6240288368831179278655173368742685790632375324962132005821718706446864372084663405911025033538700741186128949108475768232718859484959809394658838888233988", "id": "MD_6240288368831179278655173"} | MD_6240288368831179278655173 | null | [
"main_gst_gap_22_pbesol_cryst_rhom_GeTe",
"extended_gst_gap_22_for_efield_pbesol_cryst_rhom_GeTe",
"refitted_gst_gap_22_pbe_cryst_rhom_GeTe"
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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:03 | 10964077747076441951519954460639416670821703999129279283373480277351189979232838013399968738740849892587113209833426989278201680874974928563787045133648181 | CO_1096407774707644195151995 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
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| [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Ge8Sb16Te32 | GeSb2Te4 | A4B2C | [
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| true | null | null | -76,758.627026 | null | 1.224515 | 3.863833 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:36:59 | 8129299302666179993707540794154013743816491942004949363290882538943341314911515969049893059158807139766929707956159148347726086939113647748761438533521596 | PO_8129299302666179993707540 | {"config-type": "cryst", "masses": [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, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 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": "8647336201285222360204441550255211021027851447668294976531889192530462002091607031439147510636496160893015898606134443899017600666461522641020926704879401", "id": "MD_8647336201285222360204441"} | MD_8647336201285222360204441 | null | [
"main_gst_gap_22_pbesol_cryst_conv_rs_anti",
"extended_gst_gap_22_for_efield_pbesol_cryst_conv_rs_anti",
"refitted_gst_gap_22_pbe_cryst_conv_rs_anti"
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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:05 | 10966397273086159936088962369851926578492571355886433402722470510958612716140030739294543612453449263649889418305646488197831346081983680632594866830496915 | CO_1096639727308615993608896 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_cryst_spc_rs_anti",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs_anti",
"refitted_gst_gap_22_pbe_cryst_spc_rs_anti"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:06 | 11029666178088880963665240810440985015738598847590713709020558398038312147858557193881510851568064137417811818937521452094756442490276265156431344768586923 | CO_1102966617808888096366524 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_2_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_2_standard_iter",
"refitted_gst_gap_22_pbe_iter_2_standard_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 11030914495587783522795544575330690266679114588195407031655854885896763354716405816861412108082089435884667934651756480179925455806590862050423473068211809 | CO_1103091449558778352279554 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:08 | 11050818968727704128978545394595857149220951947362397470410675493213060174293997163541977434822730041983145169102524416143972534443431080649692076517928835 | CO_1105081896872770412897854 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_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 | 11082339764065485891629932403796477233460025521951886877161284243489556684379210499901158393239653977573625249006365108090812563957917456365343828597582443 | CO_1108233976406548589162993 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 11155422778512079548861253435827333425225704631708538517620756955919242415731826100670156857747437046322419337376778563121307919613083422747048618453585889 | CO_1115542277851207954886125 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| [
3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
SbTe | SbTe | AB | [
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| true | null | null | -2,951.700217 | null | 2.11489 | 2.11489 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:31:49 | 6983479588183547628203746120037876288913418740569938985429597244064826294967059816533171257207456758094028383020765741268360088573150941956365502348041164 | PO_6983479588183547628203746 | {"config-type": "dimer", "masses": [121.76, 127.6], "sub-config": "dimer", "hash": "6515008115902207792427764327069119881771508369463765552696522573764978900341385467272551511968839744382490372532353233233123211419700549454455532510838416", "id": "MD_6515008115902207792427764"} | MD_6515008115902207792427764 | null | [
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"extended_gst_gap_22_for_efield_pbesol_dimer",
"refitted_gst_gap_22_pbe_dimer"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:03 | 11200934127043708588955482004860466372026186926111412916267813009884165308438880100688400206551586982811726066966218472075766234582607817709527916232656634 | CO_1120093412704370858895548 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -233,471.739797 | null | 0.939876 | 2.901982 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:22:28 | 7158176638039406584571047926621210591869268571693383769641426002583136837881712193255254098787425349532708659041580585920871771873546024002273556423573818 | PO_7158176638039406584571047 | {"config-type": "cryst", "masses": [121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 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": "3277338474225144090537659579168049936395013639291720899674764822859438529781438429521614976060114461063477270474938672515539166826537713144541655421561518", "id": "MD_3277338474225144090537659"} | MD_3277338474225144090537659 | 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 | 11210189185158403403793543988871606057293147002244190524755787341970139050483798624288401084045710940559397303717755950254557795412048782212137472685419772 | CO_1121018918515840340379354 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
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]
]
| [
3
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| 2023 | [
0.23625529319304173,
0.21859567629705223,
0.545149030509906
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Sb76Te114 | Sb2Te3 | A3B2 | [
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Evan Ma",
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]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| [
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| 2023-12-05T20:21:03 | 11350695321853667154627354367232327667969731923425131717448141105524799038149837879748979084136483053852383691328462085118205447482257845835960373461027680 | CO_1135069532185366715462735 | GST_GAP_22_refitted | [
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"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| true | null | null | -77,376.145363 | null | 1.229403 | 6.166223 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:28:40 | 10842642612119626152580202809648451634109551594903999569418335715525406037401356869373498994255085869200992368792594843627067364743191355746538020969302921 | PO_1084264261211962615258020 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "conv_rs", "hash": "2298746989560917981779616418007549314937246874677516928516765284313710399456138179722511272556084310983731517536099024400965295487167279422884462104353557", "id": "MD_2298746989560917981779616"} | MD_2298746989560917981779616 | null | [
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"extended_gst_gap_22_for_efield_pbesol_cryst_conv_rs",
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| [
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"DS_jy3ylaf48xg3_0",
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| 2023-12-05T20:21:05 | 11376617659177067058541742783126668414136629609030636966359554356471800908695139612473999186214016133134167787695965853706747282539028821585272903956184345 | CO_1137661765917706705854174 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -90,980.826917 | null | 1.696325 | 6.649009 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:57:04 | 8389847957916068954158389289856445734280435149956703800249570990719740967967644777978816642244921591010978017494663646149021033300621416140739150656866921 | PO_8389847957916068954158389 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "conv_rs_anti", "hash": "11669784002062682397795376153279258639672969425680376277409657283026376321112150456094410281052842039439268899559747412146357774231457432252868563960545101", "id": "MD_1166978400206268239779537"} | MD_1166978400206268239779537 | null | [
"main_gst_gap_22_pbesol_cryst_conv_rs_anti",
"extended_gst_gap_22_for_efield_pbesol_cryst_conv_rs_anti",
"refitted_gst_gap_22_pbe_cryst_conv_rs_anti"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 11419479935655013365538872806914784375119225894106905810263528819302359078284003406102838063358998763460942166671467339661158225231556540593677583669472788 | CO_1141947993565501336553887 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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3
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:08 | 11517222024428325694931181625016761328470944014292317268485124744418199496283901158481163648524136252168118267734139939298508638960503291534652439288895904 | CO_1151722202442832569493118 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| [
3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| 2023-12-05T20:21:07 | 11597794286431415570639559222718497063104102297563359187788513980278234265257386019737570929205923540420546659430905596225557037951582831929929904298594575 | CO_1159779428643141557063955 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| 2023-12-05T20:21:05 | 11613572018221154010298061463689931270525580362687868666050343664629467729176253304165551605943301750511358913100266368870203529411352753975243720821599017 | CO_1161357201822115401029806 | GST_GAP_22_refitted | [
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| [
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| 2023-12-05T20:21:04 | 116401844067135962706548607021643330497579119107715264747581438296631459736370414272175403098680387379731684034719392944690173948790189675798527788234072 | CO_1164018440671359627065486 | GST_GAP_22_refitted | [
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"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
]
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"main_gst_gap_22_pbesol_cryst_spc_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs",
"refitted_gst_gap_22_pbe_cryst_spc_rs"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 11705303104759559277659991591081365003527268977737204133839849157045621869288620583809053646488740367001667606992994032451286315195470955188598334487583459 | CO_1170530310475955927765999 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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"main_gst_gap_22_pbesol_iter_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 | 11911627547217731173840158718216976759064900482726744828627212232621154839727820615407644591328839439714092592704414786557062934399574807218007899191896576 | CO_1191162754721773117384015 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| [
3
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| 2023 | [
0.23625529319304173,
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]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_2_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_2_standard_iter",
"refitted_gst_gap_22_pbe_iter_2_standard_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 11935577473271236457672696209930563006751005952374684706500404425797259745182188040935318746314139944718026287829619069682700584752999613535525069911017781 | CO_1193557747327123645767269 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| [
3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -115,152.922597 | null | 0.437683 | 1.024738 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:28:21 | 4210683550777500682516288470863031346553150930194843050429267416817647003764865052561373065733489874808463123971744945735579838270599136287560072205367820 | PO_4210683550777500682516288 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "spc_hex", "hash": "683301799929896267516051236814807644720990017798961280935293538531152424999143328536006861717775122615099718594716446691743820353277059558707142257144930", "id": "MD_6833017999298962675160512"} | MD_6833017999298962675160512 | null | [
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"extended_gst_gap_22_for_efield_pbesol_cryst_spc_hex",
"refitted_gst_gap_22_pbe_cryst_spc_hex"
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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:05 | 11952354953466179876290189912264156639888098440797862631863225196106073291692067863011003996279765882758250453327341953496809256319975650181976156262899476 | CO_1195235495346617987629018 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -77,371.952305 | null | 1.295239 | 4.859181 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:21:45 | 5832386788140970883195339877949472631247631952691430826134433672776357871581394211486370739751952095637925095307770769831353752292886776872192667969073468 | PO_5832386788140970883195339 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 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": "10430917102308429461510551410938352132224254473219674085803862430256569190219123570344754167275189643033941325669449615678788421186337821511218241471469652", "id": "MD_1043091710230842946151055"} | MD_1043091710230842946151055 | null | [
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| [
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| 2023-12-05T20:21:05 | 1200009166500893111843258717972472066774349965003963521192579574650156391346205548502605949171112841064543741768394057479779539536231899038788103768189235 | CO_1200009166500893111843258 | GST_GAP_22_refitted | [
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"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| true | null | null | -12,704.628506 | null | 1.50618 | 2.424779 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:33:43 | 775141997775944848979134442438993874394668181930467739332208023230779279927894048303546299101657313819361626348891696964918502470426786935953956207839838 | PO_7751419977759448489791344 | {"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 | 12032290527877201767575619730079141484093698432447282747147321162141074452157404134881390897326762001314648619343771703646226893221796449088171036367918198 | CO_1203229052787720176757561 | GST_GAP_22_refitted | [
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"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
]
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"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"
]
| 2023-12-05T20:21:04 | 1204468175032137881288448692541297860421449437776942610136662243232145401523349146472495993050780095698694470505483621313916760333256632194575840725279189 | CO_1204468175032137881288448 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_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 | 12049226472704042246070634985405522272872729454375845152913561375525520264912387463653757947370663744473437195609355871092342896280215390113996101182394113 | CO_1204922647270404224607063 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
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1
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| [
3
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| 2023 | [
0.23625529319304173,
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| true | null | null | -83,965.765112 | null | 2.204383 | 15.802528 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:16:10 | 10535386047796490054380125589639283559323366088139695468315726990675906655080358473746113684086350387494788620520721046888215035427711609738179226990770007 | PO_1053538604779649005438012 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "spc_hex", "hash": "13235791543569574279385427345369658452407681141389265953886517022488712993886195517946376035610230732619538486345132643668941183458936963654831828295697861", "id": "MD_1323579154356957427938542"} | MD_1323579154356957427938542 | null | [
"main_gst_gap_22_pbesol_cryst_spc_hex",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_hex",
"refitted_gst_gap_22_pbe_cryst_spc_hex"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 12138286529967812495889556470403215218483787757220956441232979481099452394097236238110736732768596933557209663095797982260732682551565667096883189454270486 | CO_1213828652996781249588955 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
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]
| [
3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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 | 12201881344923317196128027765680965020156063485366925994309325549180267007756468761709867035900252387262683430662843359466000174597092142073919843439873367 | CO_1220188134492331719612802 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -83,974.329604 | null | 1.016103 | 4.422143 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:21:15 | 2507999582816516153955072255914896749409507711558171525594041450913526898831087787118831499662644799993776455239267394064750506881018147336281919769054490 | PO_2507999582816516153955072 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "spc_hex", "hash": "13235791543569574279385427345369658452407681141389265953886517022488712993886195517946376035610230732619538486345132643668941183458936963654831828295697861", "id": "MD_1323579154356957427938542"} | MD_1323579154356957427938542 | null | [
"main_gst_gap_22_pbesol_cryst_spc_hex",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_hex",
"refitted_gst_gap_22_pbe_cryst_spc_hex"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 1220961858351158593091312693333426013382664008573924413212304508318222060039895221721893791940818315742953730052422522203437148655177984970316436319775306 | CO_1220961858351158593091312 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
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| [
3
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| 2023 | [
0.23625529319304173,
0.21859567629705223,
0.545149030509906
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Ge128Sb32Te176 | Ge8Sb2Te11 | A11B8C2 | [
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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 | 12230460565458699457598335804693175451997657240075519694346056795177493009341288468580980847098537139907541566127583088629293930633425275239486338383912320 | CO_1223046056545869945759833 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.23625529319304173,
0.21859567629705223,
0.545149030509906
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Te2 | Te | A | [
52,
52
]
| [
"Te"
]
| [
1
]
| 1 | 2 | [
[
20,
0,
0
],
[
0,
20,
0
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[
0,
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20
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]
| [
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10
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[
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| [
true,
true,
true
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| [
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[
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0,
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],
[
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"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| [
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| 2023-12-05T20:21:05 | 12285038175913817943435337018464053628788175320351024922906261905534130676795087719863597337156094968041019571387081314736449948562452700451485605191041897 | CO_1228503817591381794343533 | GST_GAP_22_refitted | [
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"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 12307105103865746908890304278944308402990670265311381019460991366937351844297756850774359253518095429800267647639441723362524430083401189453068815126395178 | CO_1230710510386574690889030 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
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| [
3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:07 | 12345850989862346473084903222019573578280199546294892722766542424399847055725392199217336418839696105079569597133365025057145634706378083538920318303148945 | CO_1234585098986234647308490 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:08 | 1235956772610602176354742537077043452086308334074699737372031751239827871189962549965244117450462022217072608331427025685990957795552737265723231976977057 | CO_1235956772610602176354742 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_3_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_3_standard_iter",
"refitted_gst_gap_22_pbe_iter_3_standard_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 12509875008367651331970946443859627130396943015453343378689877188065017337747552386539494802040438028136639384191029758618870936268186780901303010779299807 | CO_1250987500836765133197094 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| [
3
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| 2023 | [
0.23625529319304173,
0.21859567629705223,
0.545149030509906
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:07 | 12533438926296164601003813502913221881322901937120231753841225451775311286301560992761716816844625098641080120168774501004594716549430080625825192898481342 | CO_1253343892629616460100381 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -83,962.403428 | null | 2.652486 | 8.101482 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:17:25 | 10288163273308232040093897603149891650162711029353434612150182584268941187612049553647925450202254602906732740821291207434871348262627314212553988806490121 | PO_1028816327330823204009389 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "spc_hex", "hash": "13235791543569574279385427345369658452407681141389265953886517022488712993886195517946376035610230732619538486345132643668941183458936963654831828295697861", "id": "MD_1323579154356957427938542"} | MD_1323579154356957427938542 | null | [
"main_gst_gap_22_pbesol_cryst_spc_hex",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_hex",
"refitted_gst_gap_22_pbe_cryst_spc_hex"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 12556276321913634407518379468782804669975675903504484510778342022896194870059361010534335804930983064178247147319653933602233761198509276740187964996650316 | CO_1255627632191363440751837 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 12557630828798194476713997418126814670926584030724166829032997328969490906017947805171861717606099440646907589825414259108530621213994293491737621564097634 | CO_1255763082879819447671399 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
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| 2023-12-05T20:21:05 | 12645995806701398637801307268053857763250271857648191948703031879709275217697552503632916483942384400265014999813660518495347781797611113011027821606645982 | CO_1264599580670139863780130 | GST_GAP_22_refitted | [
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"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -335,913.300735 | null | 0.498675 | 1.215207 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:51:07 | 9308419526176134434901632782547665436468522837444557104494412720016554700323958897221585812134190797393162350618351589276586395806608969202972591080441009 | PO_9308419526176134434901632 | {"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, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "disorder", "hash": "11164118924248338242225734679743088548784793941046739769059912913642788667343570968873405413950856477966188293311344342703812880708728539530512207959857994", "id": "MD_1116411892424833824222573"} | MD_1116411892424833824222573 | 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 | 12650612900815169613447366503532067751020005611473543796688189612897601216732688096172457881086945418519536243973581567341254535452799877531183487668109747 | CO_1265061290081516961344736 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| [
3
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| 2023 | [
0.23625529319304173,
0.21859567629705223,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| [
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| 2023-12-05T20:21:06 | 12686177188656983837688295855219586195527612566315326080419266219970724774350384836141590705187727575392881928962780315501997744297125572919103841659085156 | CO_1268617718865698383768829 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Ge3Sb6Te12 | GeSb2Te4 | A4B2C | [
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| true | null | null | -28,787.931228 | null | 1.341916 | 3.536833 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:34:16 | 8339716619336465748745852509985099633220182060363327110141371379810514786919753232134923651987090929554758404039348595198829590013169402593575159522477169 | PO_8339716619336465748745852 | {"config-type": "cryst", "masses": [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], "sub-config": "conv_hex", "hash": "4830816949455406572169789736567860491429948757810844329915808453857771411019311101799354838419572378219825197132248055432832841489258478104957232901479312", "id": "MD_4830816949455406572169789"} | MD_4830816949455406572169789 | null | [
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"extended_gst_gap_22_for_efield_pbesol_cryst_conv_hex",
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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 12766407976404080549565275544868667610148894803676379045194860283346332500078923365870165736144580757115234731032896631891155931004702378739491149046627215 | CO_1276640797640408054956527 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
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1
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| [
3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
GeTe | GeTe | AB | [
32,
52
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| [
"Ge",
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]
| [
0.5,
0.5
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| 2 | 2 | [
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[
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[
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| [
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true,
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| [
1,
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| 3 | 2080273417940519979668719257792917683045362718722164755918850731539946317279754454826031313547117855606648286066445145512299628387650103711951400444710078 | 1 | CASTEP | PBE | null | [
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[
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| null | [
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| true | null | null | -3,110.611165 | null | 0.00459 | 0.00459 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:20:49 | 5643319556412453548858554803924831953670295234835106247030986247317708317986852178594667205210470855126787055730911184953342324298421281599600143920168061 | PO_5643319556412453548858554 | {"config-type": "cryst", "masses": [72.63, 127.6], "sub-config": "rhom_GeTe", "hash": "6240288368831179278655173368742685790632375324962132005821718706446864372084663405911025033538700741186128949108475768232718859484959809394658838888233988", "id": "MD_6240288368831179278655173"} | MD_6240288368831179278655173 | null | [
"main_gst_gap_22_pbesol_cryst_rhom_GeTe",
"extended_gst_gap_22_for_efield_pbesol_cryst_rhom_GeTe",
"refitted_gst_gap_22_pbe_cryst_rhom_GeTe"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:03 | 12778690957591110793277537623848799917713854517931006141741336765075498170939164817015224336998205788343796386418388859198764883680218442796504627786368749 | CO_1277869095759111079327753 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
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1
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| [
3
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| [
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| [
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"main_gst_gap_22_pbesol_iter_3_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_3_standard_iter",
"refitted_gst_gap_22_pbe_iter_3_standard_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 12921409721237234080509296833798979367390395604266014751447876131250451359655087828121426317422149773324716506035307270954410449585488108685145282220937414 | CO_1292140972123723408050929 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| [
3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:04 | 12922680929895843353116397006817287328479524814739777871263389215235937059505539897560222217570645848307174426252950723783479738790204971400676892608881766 | CO_1292268092989584335311639 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| 2023-12-05T20:21:04 | 13005209283478889574054299334574471590962821221662009567523149213176635788885408978507806251392272472139275071023062600181632355180358924128056340231304330 | CO_1300520928347888957405429 | GST_GAP_22_refitted | [
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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"main_gst_gap_22_pbesol_iter_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"
]
| 2023-12-05T20:21:04 | 13032325973522102788119789451589695611153834917795055469233297583063586741761546044430416360214643011891979453331823574967754976285256213912842985472574707 | CO_1303232597352210278811978 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| [
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_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 | 13114158872908966628634283562677765131407233311703126267477809045870695665723444121157590545999968797877418664656269415439140973797590822334482746463828556 | CO_1311415887290896662863428 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 1314427229643587527185862074274708443517904153498076447253479955143836306902376057329374442562995081781479792825414268475199342589388474057250620558208234 | CO_1314427229643587527185862 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
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| [
3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| [
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| 2023-12-05T20:21:03 | 13268787717003928250336105227992860342309152672216904591060613265343071421577405733501076174368097204142978716407884929775581936074349292587470798332272064 | CO_1326878771700392825033610 | GST_GAP_22_refitted | [
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]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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"main_gst_gap_22_pbesol_iter_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 | 13318672199043941422995990601836485373674175811727979669397713122838308894913726366023248768539146090224439568349761798917342121364714812158395959501484771 | CO_1331867219904394142299599 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
Ge8 | Ge | A | [
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| [
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| true | null | null | -20,306.773415 | null | 0.000429 | 0.000866 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:25:43 | 1942759275475727656331615960106064899421429165611443365945335992893163142810125621540828328643291656577284788689166172755976720003970324403792286149467104 | PO_1942759275475727656331615 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63], "sub-config": "conv_ele_cryst", "hash": "6349183166439829241359451791502533074712440869256473258456018648667318164169179950672651230309823549421286466339458593859063558119778084253780002125786382", "id": "MD_6349183166439829241359451"} | MD_6349183166439829241359451 | null | [
"main_gst_gap_22_pbesol_cryst_conv_ele_cryst",
"extended_gst_gap_22_for_efield_pbesol_cryst_conv_ele_cryst",
"refitted_gst_gap_22_pbe_cryst_conv_ele_cryst"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 1345611905717181142521039998530273599132522695948014007414887233059659775095035863819606738409666044226664600725337667344552160634287541296106731375890346 | CO_1345611905717181142521039 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
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[
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| true | null | null | -345,170.47271 | null | 3.057165 | 11.386549 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:06:52 | 2897791286817659051563938610473273385425202472193077501245569273825122390333319841832334150195631794842276006784437805912808867998065382890515793253611780 | PO_2897791286817659051563938 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63], "sub-config": "MP", "hash": "11662569143581537836262635753828942070964677130420544600702628330313113228812824370257469260781864743686245716303977505290842392299968307929823483420818320", "id": "MD_1166256914358153783626263"} | MD_1166256914358153783626263 | null | [
"main_gst_gap_22_pbesol_cryst_MP",
"extended_gst_gap_22_for_efield_pbesol_cryst_MP",
"refitted_gst_gap_22_pbe_cryst_MP"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 147939383397234458437308052903853488831885858592712142533379827593346991711015648859150606737923120329030219930948052649455115829962952391594939137141664 | CO_1479393833972344584373080 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| [
3
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Sb",
"Te"
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| 2023-12-05T20:21:06 | 1642675672932367997070106121591775994770377928730795204712183857122064075329395247557785525579430226158248001622917099233713627740174809190240144413810003 | CO_1642675672932367997070106 | GST_GAP_22_refitted | [
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"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -47,450.832565 | null | 0.617758 | 1.221241 | {"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 | 5327631641843884150778177752600967906508164373387546953875352796904918732089970331166686800135599547024864640575489141702115625535779964431945290904734578 | PO_5327631641843884150778177 | {"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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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 1662139218575584390084733856818003682968991806816684765027608546071715137046802394115611047800853003862202624135396453223313565608251303685887385588528642 | CO_1662139218575584390084733 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| [
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 1747810136869158062872376275023807398688989381422357931920370039201839909539313656773038565022003523050749570618366140678229450493150851308410242329981466 | CO_1747810136869158062872376 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
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| 2023 | [
0.23625529319304173,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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| true | null | null | -279,491.321274 | null | 0.908638 | 3.743539 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:39:30 | 12466445969837482260470753051530984084632245795122578109599476279159031065028518596259431859178148621342840194972549658125543079235873964427132969223253290 | PO_1246644596983748226047075 | {"config-type": "iter_3", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 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, 127.6, 127.6], "sub-config": "standard_iter", "hash": "12003461158681619724251028268626273354434577523969971244700726907827626353906480681204444841411131113292263304739077726025045046683902894086132665362075525", "id": "MD_1200346115868161972425102"} | MD_1200346115868161972425102 | null | [
"main_gst_gap_22_pbesol_iter_3_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_3_standard_iter",
"refitted_gst_gap_22_pbe_iter_3_standard_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 1787363479087799994389485021187642667276984610061163208206656191868500652173265423922552368642480758118614406341869200644659208679560774436474963524637242 | CO_1787363479087799994389485 | GST_GAP_22_refitted | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.23625529319304173,
0.21859567629705223,
0.545149030509906
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/164f9a70 | 7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005 | DS_jy3ylaf48xg3_0 | GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0 |
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