chemical_formula_hill
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string | chemical_formula_anonymous
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int32 | cell
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list | nperiodic_dimensions
int32 | structure_hash
string | multiplicity
int32 | software
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float64 | atomic_forces
list | atomization_energy
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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
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int64 | dataset_energy_count
int64 | dataset_energy_mean
float64 | dataset_energy_variance
float64 | dataset_formation_energy_count
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timestamp[ns] | dataset_dimension_types
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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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Ge8Sb16Te32
|
GeSb2Te4
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A4B2C
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MD_2127439833971556448789806
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PO_1404612743025509229762960
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MD_3558150408544697676330891
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CO_8453794082433862940927553
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GST_GAP_22_refitted
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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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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Te3
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Te
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MD_2127439833971556448789806
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PO_1058906091726610624727098
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MD_1268675316941029321691518
| null |
[
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[
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] | 2023-12-05T20:21:04 |
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|
CO_8463817544907240723783510
|
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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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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GeSb
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GeSb
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AB
|
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[
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PBE
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The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
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The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
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[
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The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical 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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DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge3Te3
|
GeTe
|
AB
|
[
32,
32,
32,
52,
52,
52
] |
[
"Ge",
"Te"
] |
[
0.5,
0.5
] | 2 | 6 |
[
[
4.441371,
0,
0
],
[
-2.194539,
3.801053,
0
],
[
0,
0,
11.4597
]
] |
[
[
0.07519516,
0.01492674,
8.91201388
],
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1.25775703,
1.12641975
],
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4.77997839
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2.83996577
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1.01103068,
6.85105559
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[
0.23270405,
2.75740548,
10.31775235
]
] |
[
true,
true,
true
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[
1,
1,
1
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| 1 |
CASTEP
|
PBE
| null |
[
[
0.1747,
0.27849,
-0.69527
],
[
-0.25364,
0.00794,
0.0867
],
[
-0.18249,
-0.03366,
1.19926
],
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0.33651,
-0.48515,
-0.47254
],
[
0.29165,
0.63144,
-0.9533
],
[
-0.36672,
-0.39906,
0.83516
]
] | null |
[
[
-0.026523877667555512,
-0.000025157053785732087,
-0.0021827324027471366
],
[
-0.000025157053785732087,
-0.024931137714946854,
-0.002419086615980912
],
[
-0.0021827324027471366,
-0.002419086615980912,
-0.0407016711782724
]
] | true | null | null | -9,330.740311 | null | 0.863781 | 1.213532 |
{"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"}
|
MD_2127439833971556448789806
| 2024-08-16T15:28:02 |
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|
PO_7087931353547025739125215
|
{"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 127.6, 127.6, 127.6], "sub-config": "conv_hex", "hash": "2416211705896189987138528861485508602154979060236222047577057132996305770989216649327016136327184122990563127560643768736656043656224695152297713666452863", "id": "MD_2416211705896189987138528"}
|
MD_2416211705896189987138528
| null |
[
"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 |
866475197013557100208234802671866150523709115005546828557798712497366085890331324108339950825158002837055584334265579726843827622808997693500485898046410
|
CO_8664751970135571002082348
|
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
|
Ge57Sb58Te144
|
Ge57Sb58Te144
|
A144B58C57
|
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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 |
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|
CO_8667189285502526923683152
|
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,
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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
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge20Sb82Te144
|
Ge10Sb41Te72
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A72B41C10
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| null |
[
"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:07 |
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|
CO_8695450069724201508435333
|
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
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge16Sb64Te112
|
GeSb4Te7
|
A7B4C
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| null |
[
"main_gst_gap_22_pbesol_iter_3_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_3_standard_iter",
"refitted_gst_gap_22_pbe_iter_3_standard_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
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|
CO_8698872911980212410889675
|
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
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge28Sb56Te112
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GeSb2Te4
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MD_2127439833971556448789806
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MD_8571923977142079611864080
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[
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[
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GST_GAP_22_refitted
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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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[
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{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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MD_2127439833971556448789806
| 2024-08-16T14:32:44 |
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PO_5155475137427862735082152
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MD_6240288368831179278655173
| null |
[
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[
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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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|
MD_2127439833971556448789806
| 2024-08-16T14:33:25 |
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PO_5662795706543148287440666
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|
MD_4056174476448141592851415
| null |
[
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] |
[
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"DS_jy3ylaf48xg3_0",
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CO_8927708833747693948358713
|
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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] | 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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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge80Sb20Te110
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Ge8Sb2Te11
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A11B8C2
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MD_9142202894591766066263745
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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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MD_2127439833971556448789806
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MD_1042862191277433321009227
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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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| 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"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:06 |
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CO_9033091527811627275835112
|
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
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DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge28Sb56Te112
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GeSb2Te4
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A4B2C
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MD_4440367684553027193398064
| null |
[
"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:09 |
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CO_9052320529057538639928251
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GST_GAP_22_refitted
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[
"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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] | 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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{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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GeTe
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MD_2127439833971556448789806
| 2024-08-16T14:28:05 |
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PO_5559119950425872728931630
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{"config-type": "cryst", "masses": [72.63, 127.6], "sub-config": "rhom_GeTe", "hash": "6240288368831179278655173368742685790632375324962132005821718706446864372084663405911025033538700741186128949108475768232718859484959809394658838888233988", "id": "MD_6240288368831179278655173"}
|
MD_6240288368831179278655173
| null |
[
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"extended_gst_gap_22_for_efield_pbesol_cryst_rhom_GeTe",
"refitted_gst_gap_22_pbe_cryst_rhom_GeTe"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:03 |
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CO_9092861048850588523976109
|
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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{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge16Sb10Te32
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Ge8Sb5Te16
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A16B8C5
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MD_2127439833971556448789806
| 2024-08-16T14:29:07 |
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PO_2774952113678315835806731
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MD_5073117287515818233141285
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[
"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"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:06 |
9141697002330150463728728211130556762417179855042028000211305572626724825379514921922727800506824232889120666778581268825547393295356048148568379526091568
|
CO_9141697002330150463728728
|
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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge28Sb56Te112
|
GeSb2Te4
|
A4B2C
|
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MD_2127439833971556448789806
| 2024-08-16T14:28:52 |
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PO_8641479443727304724074273
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|
MD_9357004172923400093101035
| null |
[
"main_gst_gap_22_pbesol_iter_1_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_1_standard_iter",
"refitted_gst_gap_22_pbe_iter_1_standard_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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|
CO_9188901018092225431628249
|
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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge95Te95
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GeTe
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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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Ge2Sb2Te5
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Ge2Sb2Te5
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MD_2127439833971556448789806
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PO_6561993400351834318626983
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MD_3736747359295474668976008
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[
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] | 2023-12-05T20:21:04 |
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GST_GAP_22_refitted
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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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10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Sb6Te9
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{"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"}
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MD_2127439833971556448789806
| 2024-08-16T15:16:42 |
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PO_1117636925046316320460746
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{"config-type": "cryst", "masses": [121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "MP", "hash": "11282027414001249324631233807860723523334972426598607676537831676215405476342946431415690725491398761431262216100363796890749660571007871020779168655515185", "id": "MD_1128202741400124932463123"}
|
MD_1128202741400124932463123
| null |
[
"main_gst_gap_22_pbesol_cryst_MP",
"extended_gst_gap_22_for_efield_pbesol_cryst_MP",
"refitted_gst_gap_22_pbe_cryst_MP"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:06 |
9265288067723125525506819086521020930667102383847501174660198290117892815528917824963707634423034905204688385163768400628974989567592481678849053318899930
|
CO_9265288067723125525506819
|
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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge104Sb26Te144
|
Ge52Sb13Te72
|
A72B52C13
|
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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 |
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|
CO_9265465020530189487254630
|
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
|
[
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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
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DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge43Sb44Te108
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Ge43Sb44Te108
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A108B44C43
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MD_5262879420142088628170560
| null |
[
"main_gst_gap_22_pbesol_cryst_spc_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs",
"refitted_gst_gap_22_pbe_cryst_spc_rs"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:06 |
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|
CO_9359577390340887168961402
|
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,
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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge80Sb20Te110
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Ge8Sb2Te11
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A11B8C2
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MD_1273065028643973759977561
| null |
[
"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
] |
[
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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CO_9360630824746261446405979
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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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{'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
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Sb22Te32
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Sb11Te16
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A16B11
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MD_2127439833971556448789806
| 2024-08-16T14:27:04 |
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PO_4293672899085832458623253
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MD_1228093721156287059898883
| null |
[
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] |
[
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
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CO_9406892216160426081767675
|
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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|
{'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
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DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Sb6
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Sb
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A
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CASTEP
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PBE
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|
MD_2127439833971556448789806
| 2024-08-16T15:41:34 |
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PO_6497243252502210593580998
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|
MD_1137658820933284912488164
| 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 |
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|
CO_9474272720653068772310771
|
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,
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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge54Sb36Te108
|
Ge3Sb2Te6
|
A6B3C2
|
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PO_9100928838941227944581677
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|
MD_4901330653301174584716876
| null |
[
"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:07 |
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|
CO_9641755229857847033847046
|
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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
GeSb4Te7
|
GeSb4Te7
|
A7B4C
|
[
32,
51,
51,
51,
51,
52,
52,
52,
52,
52,
52,
52
] |
[
"Ge",
"Sb",
"Te"
] |
[
0.08333333333333333,
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] | 3 | 12 |
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] |
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[
true,
true,
true
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[
1,
1,
1
] | 3 |
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| 1 |
CASTEP
|
PBE
| null |
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MD_2127439833971556448789806
| 2024-08-16T14:46:27 |
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PO_1039372112051191460933947
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{"config-type": "cryst", "masses": [72.63, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "conv_hex", "hash": "11606914213497341411008595714685281519729200534343730250725059374261891914615845843530564979381996516452893744129228585192303421822938486955566716032194708", "id": "MD_1160691421349734141100859"}
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MD_1160691421349734141100859
| null |
[
"main_gst_gap_22_pbesol_cryst_conv_hex",
"extended_gst_gap_22_for_efield_pbesol_cryst_conv_hex",
"refitted_gst_gap_22_pbe_cryst_conv_hex"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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|
CO_9666032055903365847689368
|
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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge28Sb56Te112
|
GeSb2Te4
|
A4B2C
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| null |
[
"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:08 |
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|
CO_9721799182237794678925504
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
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1
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] |
[
3
] |
2023
|
[
0.23625529319304173,
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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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge27Sb54Te108
|
GeSb2Te4
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A4B2C
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| 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 |
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|
CO_9760470795182679942679540
|
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
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge144Te144
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GeTe
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|
MD_1219171830725113859534311
| null |
[
"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:08 |
9760687765740557308617318342278662218163688083403269421729147600950967772839332068549483981454109907783781740631814945678189311870658126961549634625074269
|
CO_9760687765740557308617318
|
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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge15Sb62Te108
|
Ge15Sb62Te108
|
A108B62C15
|
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MD_2127439833971556448789806
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MD_1064850008900002283554905
| null |
[
"main_gst_gap_22_pbesol_cryst_spc_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs",
"refitted_gst_gap_22_pbe_cryst_spc_rs"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:06 |
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CO_9794163772016999254717897
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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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[
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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
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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MD_2127439833971556448789806
| 2024-08-16T15:03:14 |
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PO_7558028749653833960053346
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{"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"}
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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 |
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|
CO_9804922118206150929537368
|
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
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DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge128Sb32Te176
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Ge8Sb2Te11
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A11B8C2
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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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Ge8Sb16Te32
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MD_2127439833971556448789806
| 2024-08-16T14:21:18 |
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PO_3561424800930476938827714
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{"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"}
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| null |
[
"main_gst_gap_22_pbesol_cryst_conv_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_conv_rs",
"refitted_gst_gap_22_pbe_cryst_conv_rs"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
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|
CO_9868451489661084548953731
|
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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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
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge16Sb64Te112
|
GeSb4Te7
|
A7B4C
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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.
|
[
"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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge54Sb36Te108
|
Ge3Sb2Te6
|
A6B3C2
|
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true,
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CASTEP
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PBE
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MD_2127439833971556448789806
| 2024-08-16T15:17:32 |
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PO_1287943213624540725265497
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|
MD_1159896415082430456140656
| null |
[
"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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|
CO_9962326790899416345326836
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
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1
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] |
[
3
] |
2023
|
[
0.23625529319304173,
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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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge8
|
Ge
|
A
|
[
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32,
32,
32
] |
[
"Ge"
] |
[
1
] | 1 | 8 |
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0,
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[
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[
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] |
[
true,
true,
true
] |
[
1,
1,
1
] | 3 |
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| 1 |
CASTEP
|
PBE
| null |
[
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[
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]
] | null |
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],
[
-0.02077345969739399,
0.0837417745120761,
0.1046501419666984
]
] | true | null | null | -20,303.208741 | null | 4.292211 | 10.744649 |
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|
MD_2127439833971556448789806
| 2024-08-16T14:43:43 |
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|
PO_1072142525695590582120207
|
{"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"
] | 2023-12-05T20:21:03 |
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|
CO_9981378867776985820176975
|
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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[
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
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge95Te95
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GeTe
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AB
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| null |
[
"main_gst_gap_22_pbesol_iter_1_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_1_standard_iter",
"refitted_gst_gap_22_pbe_iter_1_standard_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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|
CO_9989929612223141582051718
|
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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge23Sb6Te32
|
Ge23Sb6Te32
|
A32B23C6
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MD_2127439833971556448789806
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MD_1325055836058455141333915
| null |
[
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"extended_gst_gap_22_for_efield_pbesol_cryst_conv_rs",
"refitted_gst_gap_22_pbe_cryst_conv_rs"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
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CO_1010210077802717090409685
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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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[
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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}
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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Te
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MD_2127439833971556448789806
| 2024-08-16T14:35:40 |
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PO_8402419764112307033901481
|
{"config-type": "cryst", "masses": [127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "spc_ele_cryst", "hash": "10482341115092695104624036464431580998332388409744383351399034763727849966709074052587122925035349033589465704752437318101099927297656895760288257555499527", "id": "MD_1048234111509269510462403"}
|
MD_1048234111509269510462403
| null |
[
"main_gst_gap_22_pbesol_cryst_spc_ele_cryst",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_ele_cryst",
"refitted_gst_gap_22_pbe_cryst_spc_ele_cryst"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
10183478050189409881950284464306266657796988956678676748735842178589026321640717244910082531293947904931696040322686026422778898123909654895712934424850889
|
CO_1018347805018940988195028
|
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
|
Ge108Te108
|
GeTe
|
AB
|
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[
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CO_1021423151747941036895285
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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
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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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge95Te95
|
GeTe
|
AB
|
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MD_2127439833971556448789806
| 2024-08-16T14:17:31 |
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PO_4535842656350374263223301
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MD_1038553750213778812552793
| null |
[
"main_gst_gap_22_pbesol_iter_2_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_2_standard_iter",
"refitted_gst_gap_22_pbe_iter_2_standard_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
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|
CO_1025391715957149530687949
|
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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Sb24Te36
|
Sb2Te3
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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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Sb8Te12
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GST_GAP_22_refitted
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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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Ge72Sb48Te144
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MD_1441031713618345506575407
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[
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[
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CO_1031722604518734608905984
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GST_GAP_22_refitted
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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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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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Ge3Sb6Te12
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GeSb2Te4
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A4B2C
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MD_2127439833971556448789806
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PO_7873736815092875644133959
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MD_4830816949455406572169789
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[
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[
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"DS_jy3ylaf48xg3_0",
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] | 2023-12-05T20:21:04 |
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CO_1048269450975232851783159
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
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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.
|
[
"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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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Te3
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Te
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PBE
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MD_2127439833971556448789806
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PO_4091835178878544793481752
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MD_1268675316941029321691518
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[
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CO_1048958096699166510967906
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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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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge28Sb56Te112
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GeSb2Te4
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A4B2C
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| null |
[
"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:09 |
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|
CO_1059300472354922733130702
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
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1
]
] |
[
3
] |
2023
|
[
0.23625529319304173,
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] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge36Sb24Te72
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Ge3Sb2Te6
|
A6B3C2
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PO_2623021759996192607453636
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MD_4849465936302868063207162
| 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 |
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|
CO_1059689848553722518456071
|
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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Sb96Te144
|
Sb2Te3
|
A3B2
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CASTEP
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PBE
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|
MD_6830794917602741578894269
| null |
[
"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:08 |
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|
CO_1068721139667674998156122
|
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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge23Sb6Te32
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Ge23Sb6Te32
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A32B23C6
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CASTEP
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PBE
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{"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"}
|
MD_2127439833971556448789806
| 2024-08-16T14:37:12 |
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PO_7152991410723305417315611
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{"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"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:06 |
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|
CO_1070129236669722364067565
|
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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge20Sb82Te144
|
Ge10Sb41Te72
|
A72B41C10
|
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MD_2127439833971556448789806
| 2024-08-16T14:15:54 |
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PO_1216146371330627673721461
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|
MD_2036576152423771349992827
| null |
[
"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:06 |
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|
CO_1076835942563718082555231
|
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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge144Te144
|
GeTe
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AB
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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 |
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|
CO_1076992097747720852487609
|
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
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Sb96Te144
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Sb2Te3
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A3B2
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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 |
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|
CO_1085641887741494031538978
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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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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
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DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge54Sb36Te108
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Ge3Sb2Te6
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A6B3C2
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[
"main_gst_gap_22_pbesol_iter_1_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_1_standard_iter",
"refitted_gst_gap_22_pbe_iter_1_standard_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
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|
CO_1086762489958368577183979
|
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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[
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
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge54Sb36Te108
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Ge3Sb2Te6
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A6B3C2
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|
MD_1159896415082430456140656
| null |
[
"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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|
CO_1090648113639706760507653
|
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,
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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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge48Sb96Te192
|
GeSb2Te4
|
A4B2C
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| 2024-08-16T14:34:41 |
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| null |
[
"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:08 |
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|
CO_1090757563500465650159876
|
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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge104Sb26Te144
|
Ge52Sb13Te72
|
A72B52C13
|
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| null |
[
"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:08 |
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CO_1091360073467599491868977
|
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
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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
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge54Sb36Te108
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Ge3Sb2Te6
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A6B3C2
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MD_1159896415082430456140656
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[
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[
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GST_GAP_22_refitted
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[
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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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{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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GeTe
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MD_2127439833971556448789806
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PO_9666436058851535503523282
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MD_6240288368831179278655173
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[
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[
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GST_GAP_22_refitted
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[
"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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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Sb72Te108
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Sb2Te3
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A3B2
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MD_2127439833971556448789806
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|
MD_7169893568233773972455451
| 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 |
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|
CO_1098917601714914220325633
|
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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Ge32Te32
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MD_9624115815842736299756762
| null |
[
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"extended_gst_gap_22_for_efield_pbesol_cryst_conv_rs",
"refitted_gst_gap_22_pbe_cryst_conv_rs"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
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CO_1099615773968921393597767
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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"
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|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge32Te32
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GeTe
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MD_2127439833971556448789806
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PO_3168956368774258325557207
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[
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
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CO_1099615773968921393597767
|
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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[
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{'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
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DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge32Te32
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GeTe
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AB
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MD_2127439833971556448789806
| 2024-08-16T14:58:19 |
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|
PO_1180146262673598602286235
|
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MD_9624115815842736299756762
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[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
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|
CO_1099615773968921393597767
|
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
]
] |
[
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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge2
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Ge
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A
|
[
32,
32
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[
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[
1
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[
true,
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1
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CASTEP
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PBE
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|
MD_2127439833971556448789806
| 2024-08-16T14:18:40 |
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PO_6445635336168581522382405
|
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|
MD_8441812449510494880668688
| null |
[
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] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:07 |
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|
CO_1101115292506543814087908
|
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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[
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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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Te3
|
Te
|
A
|
[
52,
52,
52
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[
"Te"
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[
1
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[
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true
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[
1,
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1
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CASTEP
|
PBE
| null |
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{"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"}
|
MD_2127439833971556448789806
| 2024-08-16T15:35:39 |
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PO_5966120454749019504999821
|
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|
MD_1268675316941029321691518
| null |
[
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"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 |
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|
CO_1104792487243885699703066
|
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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MD_2127439833971556448789806
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| null |
[
"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:09 |
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|
CO_1115135944343732724553922
|
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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[
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
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Sb96Te144
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Sb2Te3
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A3B2
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| null |
[
"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:06 |
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|
CO_1117802889927788953195329
|
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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge43Sb44Te108
|
Ge43Sb44Te108
|
A108B44C43
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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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GST_GAP_22_refitted
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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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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge57Sb58Te144
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Ge57Sb58Te144
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A144B58C57
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|
MD_1150382469320892083759843
| null |
[
"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:07 |
11272777027388454710858116701327352727095978586802574817621783395071926699401596597733692766553976188013240895397013489273424380588900749479568851931862515
|
CO_1127277702738845471085811
|
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,
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
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge144Te144
|
GeTe
|
AB
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MD_1219171830725113859534311
| null |
[
"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:08 |
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|
CO_1135026105463805348570236
|
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
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge48Sb32Te96
|
Ge3Sb2Te6
|
A6B3C2
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| null |
[
"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:08 |
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CO_1140060111047948367727120
|
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,
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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
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge57Sb58Te144
|
Ge57Sb58Te144
|
A144B58C57
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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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MD_2127439833971556448789806
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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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{"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"}
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{"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "conv_rs", "hash": "7404548677697602877112112683544098805929448440786777176687415762941048660247897090412802680972895990412135070434931408075297765201609940732422693568539612", "id": "MD_7404548677697602877112112"}
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| null |
[
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"extended_gst_gap_22_for_efield_pbesol_cryst_conv_rs",
"refitted_gst_gap_22_pbe_cryst_conv_rs"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
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CO_1151302398568672158804263
|
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}
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge57Sb58Te144
|
Ge57Sb58Te144
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A144B58C57
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MD_1150382469320892083759843
| null |
[
"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:07 |
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|
CO_1151873954996939974766740
|
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
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge28Sb56Te112
|
GeSb2Te4
|
A4B2C
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| 2024-08-16T15:16:20 |
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PO_6461734948864418743329571
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|
MD_4440367684553027193398064
| null |
[
"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:09 |
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|
CO_1152127290695928339300224
|
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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge80Sb20Te110
|
Ge8Sb2Te11
|
A11B8C2
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| 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 |
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CO_1152157119587417515368923
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GST_GAP_22_refitted
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[
"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
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge57Sb58Te144
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Ge57Sb58Te144
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| null |
[
"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:07 |
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|
CO_1152193899022261868769626
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
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1
]
] |
[
3
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2023
|
[
0.23625529319304173,
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] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge28Sb56Te112
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GeSb2Te4
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A4B2C
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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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| null |
[
"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:08 |
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|
CO_1160470101521000110805536
|
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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge16Sb64Te112
|
GeSb4Te7
|
A7B4C
|
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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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MD_2127439833971556448789806
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MD_6240288368831179278655173
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[
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GST_GAP_22_refitted
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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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Sb6Te3
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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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MD_2127439833971556448789806
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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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Ge27Te27
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{"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"}
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MD_2127439833971556448789806
| 2024-08-16T14:13:09 |
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PO_1176148219728142996250886
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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 |
11816829819941888985243932907083305755514317652945452168877758360415602113420273632706840523298776066698758331985554608409401062686309368283010977840479664
|
CO_1181682981994188898524393
|
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
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge36Sb72Te144
|
GeSb2Te4
|
A4B2C
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MD_2127439833971556448789806
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MD_1153744099153653775403791
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[
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[
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"DS_jy3ylaf48xg3_0",
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] | 2023-12-05T20:21:07 |
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CO_1184817750661202575756818
|
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"
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|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge23Sb6Te32
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Ge23Sb6Te32
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A32B23C6
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] | true | null | null | -90,999.035849 | null | 0.327043 | 0.696628 |
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|
MD_2127439833971556448789806
| 2024-08-16T14:14:08 |
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PO_1100463653705023688292348
|
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|
MD_1325055836058455141333915
| null |
[
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] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
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|
CO_1189198428617078643839800
|
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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GeSb4Te7
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[
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"DS_jy3ylaf48xg3_0",
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[
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The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
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|
MD_5262879420142088628170560
| null |
[
"main_gst_gap_22_pbesol_cryst_spc_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs",
"refitted_gst_gap_22_pbe_cryst_spc_rs"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:06 |
12078664005945733922207954135165818183514232277284059960689458486327687965655315625921545784135749721063220779666932439985589973876562006126823186603540589
|
CO_1207866400594573392220795
|
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,
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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge28Sb56Te112
|
GeSb2Te4
|
A4B2C
|
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| null |
[
"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:07 |
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|
CO_1222592125978228028345945
|
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
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge3Sb6Te12
|
GeSb2Te4
|
A4B2C
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"Ge",
"Sb",
"Te"
] |
[
0.14285714285714285,
0.2857142857142857,
0.5714285714285714
] | 3 | 21 |
[
[
4.392515,
0,
0
],
[
-2.045975,
3.543733,
0
],
[
-1.987056,
-1.147227,
39.197207
]
] |
[
[
0.02338952,
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],
[
1.48819068,
0.45714312,
13.32438497
],
[
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1.55284391,
26.26428454
],
[
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16.77722774
],
[
-1.00012016,
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22.56344104
],
[
1.00780691,
0.25533628,
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],
[
0.44289913,
0.10565159,
35.70873397
],
[
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],
[
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9.26684689
],
[
-0.21137201,
0.06523788,
5.41736759
],
[
-1.58047927,
-1.01423087,
33.9049961
],
[
1.27842855,
0.28022796,
18.50864676
],
[
1.82914751,
1.09710934,
7.87985372
],
[
-1.37952148,
1.48497131,
31.12677646
],
[
-0.80367259,
1.77109555,
21.13721147
],
[
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11.47278731
],
[
-1.4297845,
-0.84724962,
27.81872817
],
[
0.99621047,
0.43821086,
24.6129454
],
[
2.35038827,
1.01638605,
1.59626706
],
[
-1.74133101,
1.27907356,
37.51474528
],
[
-0.42246657,
1.92011626,
14.54220299
]
] |
[
true,
true,
true
] |
[
1,
1,
1
] | 3 |
2229025634586746059313999180318875776138955469477833084635456180740508728954958968536790710909873120558171884627134916114477742948922276067905958592020935
| 1 |
CASTEP
|
PBE
| null |
[
[
0.01332,
0.3258,
0.76364
],
[
0.23201,
2.15825,
-2.70287
],
[
1.08387,
-0.13932,
0.23395
],
[
0.22841,
-0.79767,
-0.38107
],
[
0.01094,
0.84186,
1.98667
],
[
-1.55596,
0.9641,
-4.02145
],
[
0.17798,
0.00116,
-0.97315
],
[
0.0002,
0.34099,
-0.74523
],
[
0.53838,
0.56087,
2.81815
],
[
-0.0952,
-0.77793,
0.27202
],
[
-0.41716,
0.09539,
0.26771
],
[
0.07977,
1.03631,
0.26525
],
[
-0.70784,
-0.77964,
-2.02073
],
[
1.46654,
-0.97345,
3.96267
],
[
-0.42963,
-0.95882,
-1.97969
],
[
0.79274,
0.17173,
-0.37031
],
[
-0.3437,
0.35519,
0.53161
],
[
-0.24114,
-0.31697,
-0.63168
],
[
-0.06112,
0.39992,
0.02933
],
[
0.14557,
-0.43456,
0.12929
],
[
-0.91798,
-2.07319,
2.56589
]
] | null |
[
[
0.16319235127752763,
0.014348056352977355,
0.04445884483577673
],
[
0.014348056352977355,
0.21434691137613107,
-0.007164199747547429
],
[
0.04445884483577673,
-0.007164199747547429,
0.1658640676661907
]
] | true | null | null | -28,785.348638 | null | 1.693767 | 4.418434 |
{"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"}
|
MD_2127439833971556448789806
| 2024-08-16T14:58:53 |
3169447790364234696794349252744433140543896396042143067608918578491052051901563570101609835261984086371539750519172990921531906471435369310310572534511503
|
PO_3169447790364234696794349
|
{"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 |
[
"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 |
12293355865364199548340066130064050734011774163233684615636732878135522014801751761776171394860260467056636354644558237240144574913214543979116211618743740
|
CO_1229335586536419954834006
|
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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