chemical_formula_hill
string | chemical_formula_reduced
string | chemical_formula_anonymous
string | atomic_numbers
list | elements
list | elements_ratios
list | nelements
int32 | nsites
int32 | cell
list | positions
list | pbc
list | dimension_types
list | nperiodic_dimensions
int32 | structure_hash
string | multiplicity
int32 | software
string | method
string | adsorption_energy
float64 | atomic_forces
list | atomization_energy
float64 | cauchy_stress
list | cauchy_stress_volume_normalized
bool | electronic_band_gap
float64 | electronic_band_gap_type
string | energy
float64 | formation_energy
float64 | max_force_norm
float64 | mean_force_norm
float64 | property_object_metadata
string | property_object_metadata_id
string | property_object_last_modified
timestamp[ns] | property_object_hash
string | property_object_id
string | configuration_metadata
string | configuration_metadata_id
string | configuration_labels
list | configuration_names
list | configuration_dataset_ids
list | configuration_last_modified
timestamp[ns] | configuration_hash
string | configuration_id
string | dataset_name
string | dataset_authors
list | dataset_description
string | dataset_elements
list | dataset_nelements
int32 | dataset_nproperty_objects
int64 | dataset_nconfigurations
int32 | dataset_nsites
int64 | dataset_adsorption_energy_count
int64 | dataset_atomic_forces_count
int64 | dataset_atomization_energy_count
int64 | dataset_cauchy_stress_count
int64 | dataset_electronic_band_gap_count
int64 | dataset_energy_count
int64 | dataset_energy_mean
float64 | dataset_energy_variance
float64 | dataset_formation_energy_count
int64 | dataset_last_modified
timestamp[ns] | dataset_dimension_types
list | dataset_nperiodic_dimensions
list | dataset_publication_year
string | dataset_total_elements_ratios
list | dataset_license
string | dataset_links
string | dataset_doi
string | dataset_hash
string | dataset_id
string | dataset_extended_id
string |
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"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
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| 2023-12-05T20:21:06 | 10015410670660899524265045551556492793003727651818195141379544405458443049076013366620409357382357530639948501250519337186501638335662995233304196712301844 | CO_1001541067066089952426504 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| true | null | null | -114,349.481286 | null | 1.235593 | 6.178488 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:30:47 | 8042362502721760352066374845919063853427436146930540693675557737205208878566275259165532244231944659344837250247451637557415803485824622247272451653910058 | PO_8042362502721760352066374 | {"config-type": "cryst", "masses": [121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76], "sub-config": "spc_ele_cryst", "hash": "2715333019700714844145994706090996120496929084575008822287006054578880522607264016889143281893607225757542324686604781612875117960083345583094603658552292", "id": "MD_2715333019700714844145994"} | MD_2715333019700714844145994 | null | [
"main_gst_gap_22_pbesol_cryst_spc_ele_cryst",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_ele_cryst",
"refitted_gst_gap_22_pbe_cryst_spc_ele_cryst"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 10038368576131921779246092365816934118299652593937369303353780789289267004619041190614461619200031656299991848535669079244609173406541931493497977158060535 | CO_1003836857613192177924609 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
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1
]
]
| [
3
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| 2023 | [
0.23630478379677952,
0.21855465772221375,
0.5451405584810067
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| [
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| [
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"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:07 | 1004584479659665390790256040016764083380873781916303878013392410425563025888111875195043776957630914952964879311005311709070988898582258273411212608984601 | CO_1004584479659665390790256 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| true | null | null | -76,616.040055 | null | 1.356772 | 3.983842 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:20:08 | 4227031039257530979694206819226865403994932947530871247115858274462469356374094077639760327875307697912217476303926928185847397785583437763915724270092986 | PO_4227031039257530979694206 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "conv_rs", "hash": "3558150408544697676330891047416421949637381360072465706128507139136048775706449967444597095304685706452033314150590934437672173605413493948631421337772505", "id": "MD_3558150408544697676330891"} | MD_3558150408544697676330891 | null | [
"main_gst_gap_22_pbesol_cryst_conv_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_conv_rs",
"refitted_gst_gap_22_pbe_cryst_conv_rs"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 10047938266282435449555545971732008211033823452388325953470257285002410787892151985522144357430726465934566587767934153007840314494159192561725136149719992 | CO_1004793826628243544955554 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
3
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
GeTe | GeTe | AB | [
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52
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| [
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| [
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| 2023-12-05T20:21:03 | 10108994754413055527165652588452379254307858887520431407586790313088938554357260331185110566486623746030587921074124308343651496592667888724674615734825440 | CO_1010899475441305552716565 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Ge24 | Ge | A | [
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| true | null | null | -60,883.257536 | null | 4.694365 | 11.369476 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:21:38 | 5263623763965712271531697012813341407477955757626494226931582200683365204764825011929007224265825601498404025015908918757571500116939892962440186794125802 | PO_5263623763965712271531697 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63], "sub-config": "MP", "hash": "11866058122228346954156734186532938803390128345757456036454223089724601513710565740270638902699779000674816554979282675272247382687626689395943307742250178", "id": "MD_1186605812222834695415673"} | MD_1186605812222834695415673 | null | [
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| [
"DS_efcq7a0h6z5w_0",
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]
| 2023-12-05T20:21:07 | 10132928919922277923033609097124969264446068319716924358641083639305026722138458562955714779688728726567687729106367471950734856991367710219420651113481283 | CO_1013292891992227792303360 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Sb2 | Sb | A | [
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| [
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| [
1
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| null | []
| null | null | null | -4,757.717848 | null | 1.7108 | 1.7108 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:12:43 | 12517847513988176487643808243668221944969107050053133403006528568792371242134953378372919349493341088547974192018618728550834799772171961862305239279033090 | PO_1251784751398817648764380 | {"config-type": "dimer", "masses": [121.76, 121.76], "sub-config": "dimer", "hash": "12749784377554201583291612343634491581939460251612151514457680212018420488370028909551115401499445089420899938851942171239271722616911573405893787570700809", "id": "MD_1274978437755420158329161"} | MD_1274978437755420158329161 | null | [
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]
| [
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]
| 2023-12-05T20:21:03 | 10146059375986250504870488571345713926049647549889030623508600863668808337711466258243716308022788005656793174691496117989148452793569707637137242566995373 | CO_1014605937598625050487048 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
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1
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| [
3
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
GeTe | GeTe | AB | [
32,
52
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| [
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]
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0.5,
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| true | null | null | -3,106.429093 | null | 0.545906 | 0.545906 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:12:12 | 6711660349589722358359776870896680700748326595592019609345800295444289587268331082695192540761468419249721712254396720914041933933048848159674758083073314 | PO_6711660349589722358359776 | {"config-type": "cryst", "masses": [72.63, 127.6], "sub-config": "rhom_GeTe", "hash": "6240288368831179278655173368742685790632375324962132005821718706446864372084663405911025033538700741186128949108475768232718859484959809394658838888233988", "id": "MD_6240288368831179278655173"} | MD_6240288368831179278655173 | null | [
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"extended_gst_gap_22_for_efield_pbesol_cryst_rhom_GeTe",
"refitted_gst_gap_22_pbe_cryst_rhom_GeTe"
]
| [
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]
| 2023-12-05T20:21:03 | 10146845294111890822044163018401183412485917806933309250310189609754874491888370338189387970920565813591214993724184695332672819313935371232677550915427405 | CO_1014684529411189082204416 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
1,
1
]
]
| [
3
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| 2023 | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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Sb2 | Sb | A | [
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| 2023-12-05T20:21:03 | 10161161308364975121308973723040085540163110096679998044296944543864982108927420088937219538816787035413886924321713652987825828642262896097978955195521345 | CO_1016116130836497512130897 | GST_GAP_22_main | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
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"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 10197341453079613284485701846508708377344201871123684479717774705673175483931008726380818740306807224052539013560613532224658410565813646018227427079323388 | CO_1019734145307961328448570 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
3
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"main_gst_gap_22_pbesol_iter_2_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_2_standard_iter",
"refitted_gst_gap_22_pbe_iter_2_standard_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 10253917159571495306879499051757447476420997963993428770306404670227880830551950851506992677783424211940432734064277072131211404643390669833984429988290651 | CO_1025391715957149530687949 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
3
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Ge80Sb20Te110 | Ge8Sb2Te11 | A11B8C2 | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| true | null | null | -25,884.633946 | null | 1.171457 | 3.128882 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:24:45 | 10657792027387853140344747584996790271873757700383574715701431848658910055308519283892194183582237386729161373679972306794904616242851782768201138018546577 | PO_1065779202738785314034474 | {"config-type": "cryst", "masses": [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], "sub-config": "MP", "hash": "6912131359980503114559440824891231363178290412219599235448613152616524499652098042309091007818190153911241511825303639870847720218991660602043910200489593", "id": "MD_6912131359980503114559440"} | MD_6912131359980503114559440 | null | [
"main_gst_gap_22_pbesol_cryst_MP",
"extended_gst_gap_22_for_efield_pbesol_cryst_MP",
"refitted_gst_gap_22_pbe_cryst_MP"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 10294811881261124789895102114406624825318966766199022673471775808125957854145132209049740414109699339944242072514006374271062066242939778580865742285809669 | CO_1029481188126112478989510 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
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| [
3
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Ge72Sb48Te144 | Ge3Sb2Te6 | A6B3C2 | [
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"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
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| [
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"DS_jy3ylaf48xg3_0",
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| 2023-12-05T20:21:08 | 10317226045187346089059846280947591400281071839212414542881128356726489219534270230907933110236547026817986529418621863284252005830193551485029942416155908 | CO_1031722604518734608905984 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| true | null | null | -71,229.914411 | null | 1.505243 | 4.271382 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:24:40 | 6139902839132220405051114956435618456325217803396844995172943555014329269586755808313845068979361986568455188941230538012989546304941489467098567132788719 | PO_6139902839132220405051114 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "conv_rs", "hash": "7583248193253721435988354141533753145514240242908978894587514962002194664664794927268285074587946246183169942703624997915635946341177724680919346281268912", "id": "MD_7583248193253721435988354"} | MD_7583248193253721435988354 | null | [
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"extended_gst_gap_22_for_efield_pbesol_cryst_conv_rs",
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| 2023-12-05T20:21:04 | 10377757571106682339412351722756190771842128804967392185032036717683946709346254077140087187756606771863986530771353967123281623515465870531867058864805863 | CO_1037775757110668233941235 | GST_GAP_22_main | [
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]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
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"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 10393989086157390121085484108445565511808136223812439938985279741074274255068525319466204979555669746709052242599268059198389914157034205262491599489220234 | CO_1039398908615739012108548 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
3
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| true | null | null | -82,628.303171 | null | 0.386689 | 0.924584 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:13:33 | 4771479201993220627471278236911032873816838599283174199214497375738687626593165687803771660843467347755135901748995011302643927603575517022022189286395914 | PO_4771479201993220627471278 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "conv_rs_anti", "hash": "5073117287515818233141285278410823651751301141196327268537929970241044359457776115326502921231676853878585816727795291763981466751934393845657244040793901", "id": "MD_5073117287515818233141285"} | MD_5073117287515818233141285 | null | [
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"extended_gst_gap_22_for_efield_pbesol_cryst_conv_rs_anti",
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| [
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"DS_jy3ylaf48xg3_0",
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| 2023-12-05T20:21:06 | 10480949527083914011524990623662518242642943680064873631025801996848842580963846685182872046273674838985631051044746587561172963119022905609395486172549454 | CO_1048094952708391401152499 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 10486921683336771756601512094673092320721792964475544735821517076990523662986082184151740467345861290088472410632907933604078935611153346437314873163390231 | CO_1048692168333677175660151 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| true | null | null | -284,202.573976 | null | 0.608974 | 1.540967 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:35:08 | 6682404879985648541893889985651952073639054432592221088267416063392913076655522118365726127768640928470048421942835291311411686041030133770215844195299789 | PO_6682404879985648541893889 | {"config-type": "iter_3", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "standard_iter", "hash": "13076341231956572762013883357636141963647692326548027296652024507445029781092273575141931804623871693304300951628777377475710969238202272037232586218948202", "id": "MD_1307634123195657276201388"} | MD_1307634123195657276201388 | 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"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:06 | 1053601007959690342749012866555296290758110609903982189941420795197747376820120359147566726345709148752626461501516230997645466551655975783206689938790046 | CO_1053601007959690342749012 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
3
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:04 | 10544022995229004615077493135406439589194949686216104103206226979119890291371575606487323993812700536237307002233320178294544079096737991031560976585933023 | CO_1054402299522900461507749 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| 2023-12-05T20:21:04 | 10620460727542840923567467456586705748914327254750393157849763677554032867885285297815296834275631289746469019141460141716987352404505955588210767005347222 | CO_1062046072754284092356746 | GST_GAP_22_main | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| true | null | null | -77,241.927765 | null | 0.493169 | 1.029995 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:13:33 | 11545047844940059571576251055986611585860780362903179217153800820970443795816015443202688733478025773789766961534287080897621189555784031995097539761006321 | PO_1154504784494005957157625 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "conv_rs", "hash": "2298746989560917981779616418007549314937246874677516928516765284313710399456138179722511272556084310983731517536099024400965295487167279422884462104353557", "id": "MD_2298746989560917981779616"} | MD_2298746989560917981779616 | null | [
"main_gst_gap_22_pbesol_cryst_conv_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_conv_rs",
"refitted_gst_gap_22_pbe_cryst_conv_rs"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:05 | 10645552342280071650465708685313622715552889683433548166231973106108642786728140994291590173754460364482836190385175946189026008110662244126448719675868197 | CO_1064555234228007165046570 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
3
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Ge16Sb64Te112 | GeSb4Te7 | A7B4C | [
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"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 10653112447392243633913420230215617791067203546516656219629043812170609405860185203848000694741622683489205867696406113759244515866279559436723013155853964 | CO_1065311244739224363391342 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023-12-05T20:21:07 | 10654905499756935822506088525758606947818160877542395433576379204966304547087779972978266360050679443532181164060944082417604536431231074806757995411914857 | CO_1065490549975693582250608 | GST_GAP_22_main | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| [
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| 2023-12-05T20:21:05 | 10739187353221502976162580272231544971066962250787720477950533050926904292207344540984115256554244220224211675469646476494928600008997084282044258856007045 | CO_1073918735322150297616258 | GST_GAP_22_main | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
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| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
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| 2023-12-05T20:21:07 | 10740886016099042104159133199715655989436779774128084975497185829493275118763549789170941181727076850019541412599811790166254368566187144098634852439513703 | CO_1074088601609904210415913 | GST_GAP_22_main | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| [
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| 2023-12-05T20:21:03 | 10781509806942056139480017200744787485059103470554641221496830164597181426642682879260500157780176585626426758150903039691800068015913527442288069998497670 | CO_1078150980694205613948001 | GST_GAP_22_main | [
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"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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"main_gst_gap_22_pbesol_iter_2_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_2_standard_iter",
"refitted_gst_gap_22_pbe_iter_2_standard_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 10799099789394663581204116777030461949367819521838256065893134015391743243561739569364344953982064398930153661801631208003565130580525861416880338456876219 | CO_1079909978939466358120411 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 10856418877414940315389786872078838850805050564133065599452567865508333928062923142658951050720449374048678238208825365725745898898207790229644853399879887 | CO_1085641887741494031538978 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:08 | 10858804880380288515900109161097887224173440984962366930387913043740910054586247949121511859536891304917799718006678932933285632754947039137613889115197904 | CO_1085880488038028851590010 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
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| [
3
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"main_gst_gap_22_pbesol_iter_1_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_1_standard_iter",
"refitted_gst_gap_22_pbe_iter_1_standard_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 10903459052337321814489500588672304138553136526261188569196036878203552640583948290855325718443905056232494903390979931664460365027871418609993801220657494 | CO_1090345905233732181448950 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Evan Ma",
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]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:09 | 10943116829179789921860753272479975044350732625964228411396182872510965667123214778509338777051460274567180721801489838115316994802500600945032719212130996 | CO_1094311682917978992186075 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| true | null | null | -247,199.436955 | null | 1.01421 | 3.216987 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:10:07 | 2823706638029547815387307309864362598372045317573616660146392661656576066576031245914938514778012190459707062862753700686172967982657028390235358680680283 | PO_2823706638029547815387307 | {"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, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "spc_rs_anti", "hash": "4347548186909960830854754339326917425031081498973565753214976579551612239551187468974219703780510825542504223980005237190768245435782984346372223535225930", "id": "MD_4347548186909960830854754"} | MD_4347548186909960830854754 | null | [
"main_gst_gap_22_pbesol_cryst_spc_rs_anti",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs_anti",
"refitted_gst_gap_22_pbe_cryst_spc_rs_anti"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 11001903927225005693487928652029839173736388886486114521433748955882484156257071425101692034935505725490506600999816202499287155514178051496998130910001374 | CO_1100190392722500569348792 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
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| [
3
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| [
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| [
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"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:08 | 11008635787712512287453059430914925086193405360774754698198854476522803527931641253323004991276973283836180006896359062053043568052220834225886685599248753 | CO_1100863578771251228745305 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| true | null | null | -62,298.865231 | null | 1.345498 | 2.830369 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:12:57 | 13232602022181786726129409136197272487897654746772220164878103029435752120724270632694149947093575849666588785261061833721256802423223960548405939174154488 | PO_1323260202218178672612940 | {"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, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "MP", "hash": "12985505135473005445786382180529783616003266646389292197976939388946391379988894842168260452578529222033060642475184627399944959469779063643954001217397079", "id": "MD_1298550513547300544578638"} | MD_1298550513547300544578638 | null | [
"main_gst_gap_22_pbesol_cryst_MP",
"extended_gst_gap_22_for_efield_pbesol_cryst_MP",
"refitted_gst_gap_22_pbe_cryst_MP"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 11035728375156714525042621606741453419453774711493522065610462001053790157828519457619272817782326079909410290420678610199265101532369695820969235810214995 | CO_1103572837515671452504262 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| 2023-12-05T20:21:08 | 11041679304625833187917633980464645712729731019022777246028971529974683022584054863642906744499586252528264896978180564988573977219513216609810019578671855 | CO_1104167930462583318791763 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.23630478379677952,
0.21855465772221375,
0.5451405584810067
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Sb96Te144 | Sb2Te3 | A3B2 | [
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| [
"Sb",
"Te"
]
| [
0.4,
0.6
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"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:08 | 11046443996096386759509064016831261844160534959732318212618041411675563932760748395576211963481044312225066414278313917182487170780348730704215631092255924 | CO_1104644399609638675950906 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
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1
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| [
3
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| 2023 | [
0.23630478379677952,
0.21855465772221375,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| [
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| [
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"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:09 | 11063725269053690760223331403709974817163612506200298408069191094157461233256650897737319028215331060259771472420581343895873072004701140696528730107852344 | CO_1106372526905369076022333 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"main_gst_gap_22_pbesol_iter_1_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_1_standard_iter",
"refitted_gst_gap_22_pbe_iter_1_standard_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 11064099772458414625380745620317693345588957402992749851048260073778103529133224022719812467597630630911901750667394270806487475156840468151087335211171377 | CO_1106409977245841462538074 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| true | null | null | -9,529.208588 | null | 0.055001 | 0.083045 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:11:47 | 5843266828938552805464515886028214671913746255319558423645156846910933080404756375082063946535229500441963644122626289851025325380721199314882182521576393 | PO_5843266828938552805464515 | {"config-type": "cryst", "masses": [121.76, 121.76, 121.76, 121.76], "sub-config": "MP", "hash": "10708469732072958718894436945396842459064405508219097911751311763812830399053117687400639804661591936716780978275210903190947838316693035825745598475987403", "id": "MD_1070846973207295871889443"} | MD_1070846973207295871889443 | null | [
"main_gst_gap_22_pbesol_cryst_MP",
"extended_gst_gap_22_for_efield_pbesol_cryst_MP",
"refitted_gst_gap_22_pbe_cryst_MP"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 11096684257401716202008127382407136459606911503132671956425854327896837442261615162429525938302391282090612234400993036204492620400004827270508287043898119 | CO_1109668425740171620200812 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 1111362383409169222180534224838656325075113638498082586380373429261334996385085200254837938983265528368723633657513776153952092174476418273259802967529396 | CO_1111362383409169222180534 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:07 | 11119717814908681899518769158552053789355103774074228292743884837209712902092490655009759516256629724987687859286500387313312354974212449585230108990485311 | CO_1111971781490868189951876 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| 2023-12-05T20:21:04 | 11129697299922861917148792343676361150341248603819265323610647604591071116109328407274910125628308015262732674161369547801012878142521392339074559675145166 | CO_1112969729992286191714879 | GST_GAP_22_main | [
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"Evan Ma",
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023-12-05T20:21:06 | 11178028899277889531953299070892548543901405462071081138701884956473127657612290862712237349816069513694922916448422258728317944280916781245236455439814766 | CO_1117802889927788953195329 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
3
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| true | null | null | -275,364.235782 | null | 0.883625 | 3.298475 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:36:18 | 2731601021640284058226338231578554683637282484962039995591409606778871527049937316849607987287469757409237452680232510563926596770398942468279855493791431 | PO_2731601021640284058226338 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "spc_rs_anti", "hash": "13383993830272605086388361181036213913836593156340899682449997787345260753475138808026642762789139523677928004076965232067495120884298268355388559603176711", "id": "MD_1338399383027260508638836"} | MD_1338399383027260508638836 | null | [
"main_gst_gap_22_pbesol_cryst_spc_rs_anti",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs_anti",
"refitted_gst_gap_22_pbe_cryst_spc_rs_anti"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 11216542614804468124847275997221775989156229586770331160698240178601344114283967773435043353703214684349015528339234822399445012294861926881719839506293560 | CO_1121654261480446812484727 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.23630478379677952,
0.21855465772221375,
0.5451405584810067
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Ge9Sb6Te18 | Ge3Sb2Te6 | A6B3C2 | [
32,
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52,
52,
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| [
"Ge",
"Sb",
"Te"
]
| [
0.2727272727272727,
0.18181818181818182,
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| 3 | 33 | [
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| true | null | null | -47,368.947821 | null | 0.831337 | 2.097137 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:11:53 | 8314283304390682014107392931712857482698096936164776689885230331276443873707798398006383131976811756386324845831450250413594183034494811734850676322638046 | PO_8314283304390682014107392 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "conv_hex", "hash": "3671207608404505370682589606096776184028544354012001044982517601694972033807052280135612196966234880039393893029496696662628098506552426897096802559474888", "id": "MD_3671207608404505370682589"} | MD_3671207608404505370682589 | null | [
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"extended_gst_gap_22_for_efield_pbesol_cryst_conv_hex",
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| [
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"DS_jy3ylaf48xg3_0",
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| 2023-12-05T20:21:05 | 11255029297785386420163868070685789265671516031681422632262443835837832125085907900598226898564205183605558673375923176864949016985546034418416110359035924 | CO_1125502929778538642016386 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:07 | 11272777027388454710858116701327352727095978586802574817621783395071926699401596597733692766553976188013240895397013489273424380588900749479568851931862515 | CO_1127277702738845471085811 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"main_gst_gap_22_pbesol_cryst_spc_rs_anti",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs_anti",
"refitted_gst_gap_22_pbe_cryst_spc_rs_anti"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 11353264232979298068760616386533783259519173118597639257078205802265606569068653811589698492710612364490700046537662247306511637868882393933705774392452110 | CO_1135326423297929806876061 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
3
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"refitted_gst_gap_22_pbe_cryst_spc_rs_anti"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 11428507646117550390791126951700975423691873102520696253937524921689472009025105981433825641374349473243517152082947617486424538343106454243233139786902408 | CO_1142850764611755039079112 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
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| [
3
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| true | null | null | -284,181.990441 | null | 0.82402 | 2.977924 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:25:26 | 10930413428819766228238707571774188675053896197427147411740545728118644919902050452736964210894955664644872938546617053750753926635295269617664127661959339 | PO_1093041342881976622823870 | {"config-type": "iter_2", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "standard_iter", "hash": "12569664586464173906703982126356212601492098174206558404616938390977761147256497637236415261499240553614759978585425986034807138052593011394007811251201179", "id": "MD_1256966458646417390670398"} | MD_1256966458646417390670398 | 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 | 11440751221374839792806991643719453187268895819737535248777042188494284797099236715280659665379755590755802877252526765891406458755738029076588557804007440 | CO_1144075122137483979280699 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.23630478379677952,
0.21855465772221375,
0.5451405584810067
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Sb24Te36 | Sb2Te3 | A3B2 | [
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| [
"Sb",
"Te"
]
| [
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| 2 | 60 | [
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| [
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"DS_jy3ylaf48xg3_0",
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]
| 2023-12-05T20:21:07 | 11471205504799837451746620689029011935980886649199053530624184108474693599844520552486745190170741273029743006009630079174947824913302288738363017945852881 | CO_1147120550479983745174662 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
GeTe | GeTe | AB | [
32,
52
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| [
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"extended_gst_gap_22_for_efield_pbesol_dimer",
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| [
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"DS_jy3ylaf48xg3_0",
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]
| 2023-12-05T20:21:03 | 11498973690236574656331462283986587366578863465379527231562562659011457688072059979227139917510486544183791169332470040847727218212550738555297919073361462 | CO_1149897369023657465633146 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Ge27Te27 | GeTe | AB | [
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| true | null | null | -83,866.569242 | null | 0.979466 | 2.427455 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:32:46 | 3818988158663119894269408084964475048424192288803473342303658552257983342108936911075439694190928130201002130706611674916291653986059899979177534722833175 | PO_3818988158663119894269408 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "spc_hex", "hash": "13235791543569574279385427345369658452407681141389265953886517022488712993886195517946376035610230732619538486345132643668941183458936963654831828295697861", "id": "MD_1323579154356957427938542"} | MD_1323579154356957427938542 | null | [
"main_gst_gap_22_pbesol_cryst_spc_hex",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_hex",
"refitted_gst_gap_22_pbe_cryst_spc_hex"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:05 | 11502515723823985292891435604585025129415065925095710974916066726214014391870714261659989345852812746244735508441646091314149799715439622833135677436854438 | CO_1150251572382398529289143 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:04 | 11516338845394131987687977325377528677584914373268133233236527752208000655050346476680980109799493066365873962821645654681071675986030932860898709870268925 | CO_1151633884539413198768797 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 11518739549969399747667408442766697642623936511662829180261620240992700167976607035541351835578818596823585489972700701220213749156138428661913181592544221 | CO_1151873954996939974766740 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
3
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 11521938990222618687696266122497325414594417865821701933438081613419243964551173821473809908075118492321146134913491236596844814994003875390311298259057751 | CO_1152193899022261868769626 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
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| [
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Sb6Te9 | Sb2Te3 | A3B2 | [
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| [
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| [
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| 2 | 15 | [
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| true | null | null | -19,413.652604 | null | 0.8532 | 1.849548 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:24:42 | 3671550239593172810376138257558925132727477389279641122450736131183742362932065365777630745906712266885080878399206597638746907395245376213033251265360643 | PO_3671550239593172810376138 | {"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": "conv_hex", "hash": "1867299584880752190154480917848328645830387837128504546820673793588381302748653972765633595421703139018408419208992678070013215600954406901262905491680318", "id": "MD_1867299584880752190154480"} | MD_1867299584880752190154480 | null | [
"main_gst_gap_22_pbesol_cryst_conv_hex",
"extended_gst_gap_22_for_efield_pbesol_cryst_conv_hex",
"refitted_gst_gap_22_pbe_cryst_conv_hex"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:04 | 11524463240806315651940179926143692011748448702049994807765345893080079212657212924056467920918620429441134377930041965077736284901221931847783469360737742 | CO_1152446324080631565194017 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"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 | 11605776695240471605731382712809672022814134218762818319818729851598632801894388461642785603689910294439049611757079978025717188199044286004626994510539146 | CO_1160577669524047160573138 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
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| [
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Ge3Sb6Te12 | GeSb2Te4 | A4B2C | [
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| [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"main_gst_gap_22_pbesol_cryst_spc_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs",
"refitted_gst_gap_22_pbe_cryst_spc_rs"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:06 | 11705303104759559277659991591081365003527268977737204133839849157045621869288620583809053646488740367001667606992994032451286315195470955188598334487583459 | CO_1170530310475955927765999 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
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| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:09 | 11705977629719563885882826300969905979234079478677605530188869326513878650353583622847929631671550395157489812175714964545533082731646342913085877146189109 | CO_1170597762971956388588282 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
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1
]
]
| [
3
]
| 2023 | [
0.23630478379677952,
0.21855465772221375,
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]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Ge54Sb36Te108 | Ge3Sb2Te6 | A6B3C2 | [
32,
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| null | [
[
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| true | null | null | -284,203.947053 | null | 0.536569 | 1.192929 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:36:40 | 2413237189104449376366360873030369539551368000710028739961173668802897854082081587042405685127049444847989217529241381534938851867269826741211201385563374 | PO_2413237189104449376366360 | {"config-type": "iter_2", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "standard_iter", "hash": "12569664586464173906703982126356212601492098174206558404616938390977761147256497637236415261499240553614759978585425986034807138052593011394007811251201179", "id": "MD_1256966458646417390670398"} | MD_1256966458646417390670398 | 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 | 11739288433591455581758973056384340528853846006995583778222163278548243989887739768814971038867443680698397879885691264767421331177303173915885834038296220 | CO_1173928843359145558175897 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.23630478379677952,
0.21855465772221375,
0.5451405584810067
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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