Dataset Viewer
chemical_formula_hill
string | chemical_formula_reduced
string | chemical_formula_anonymous
string | atomic_numbers
sequence | elements
sequence | elements_ratios
sequence | nelements
int32 | nsites
int32 | cell
sequence | positions
sequence | pbc
sequence | dimension_types
sequence | nperiodic_dimensions
int32 | structure_hash
string | multiplicity
int32 | software
string | method
string | adsorption_energy
float64 | atomic_forces
sequence | atomization_energy
float64 | cauchy_stress
sequence | 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
sequence | configuration_names
sequence | configuration_dataset_ids
sequence | configuration_last_modified
timestamp[ns] | configuration_hash
string | configuration_id
string | dataset_name
string | dataset_authors
sequence | dataset_description
string | dataset_elements
sequence | 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
sequence | dataset_nperiodic_dimensions
sequence | dataset_publication_year
string | dataset_total_elements_ratios
sequence | 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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| null | [
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| true | null | null | -335,420.727151 | null | 0.722363 | 2.049729 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:26:10 | 11053358952067385136148461901419007734499411874414444338423145537171337011830826500473629360333358943836160427927627184736852230191898202089125839536616736 | PO_1105335895206738513614846 | {"config-type": "aimd", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "disorder", "hash": "11164118924248338242225734679743088548784793941046739769059912913642788667343570968873405413950856477966188293311344342703812880708728539530512207959857994", "id": "MD_1116411892424833824222573"} | MD_1116411892424833824222573 | null | [
"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 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 | [
[
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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Dataset
GST GAP 22 main
Description
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.
Additional details stored in dataset columns prepended with "dataset_".
Dataset authors
Yuxing Zhou, Wei Zhang, Evan Ma, Volker L. Deringer
Publication
https://doi.org/10.1038/s41928-023-01030-x
Original data link
https://doi.org/10.5281/zenodo.8208202
License
CC-BY-4.0
Number of unique molecular configurations
2692
Number of atoms
341068
Elements included
Ge, Sb, Te
Properties included
energy, atomic forces, cauchy stress
Cite this dataset
Zhou, Y., Zhang, W., Ma, E., and Deringer, V. L. GST GAP 22 main. ColabFit, 2023. https://doi.org/10.60732/f2d6e02c
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