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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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| 2023-12-05T20:21:05 | 8372273029205403023327197946312370512004756536752373591280132085037455094523770854347640234244919115075027400670438559593580853291879768034088524773003673 | CO_8372273029205403023327197 | 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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"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 | 8379309156483792059131366808026293017530763036036398223765353128294145213002419644404876766089575417845927387169326026224703201484461800130857964038223906 | CO_8379309156483792059131366 | 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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"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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| true | null | null | -30,701.829762 | null | 1.932871 | 6.016641 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:28:01 | 7553386791870207594853284048483061455140983088370723870225239282236912836788771946558030532009195894876397979191906794982583988488669109832160153114959227 | PO_7553386791870207594853284 | {"config-type": "cryst", "masses": [127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "spc_ele_cryst", "hash": "10482341115092695104624036464431580998332388409744383351399034763727849966709074052587122925035349033589465704752437318101099927297656895760288257555499527", "id": "MD_1048234111509269510462403"} | MD_1048234111509269510462403 | null | [
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| [
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| 2023-12-05T20:21:04 | 8400946540752667872058231078485437019601742969624826143327126814052702896158845210949639734207482092222226621148877538813789064038440335503469632766257514 | CO_8400946540752667872058231 | 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 |
Sb48 | Sb | A | [
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| true | null | null | -114,356.122669 | null | 1.305993 | 4.777826 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:03:12 | 9972476241443625715588077773398434036574706641638784228125080500780838424421588419272374441573816569923477374152436961118651707932813724713633038288716569 | PO_9972476241443625715588077 | {"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"
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| [
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:04 | 8420002516656263024478298389795005222040607351700022642973502996396041656354970106130394949441035402738205824225607056830708715822016463367980628304920203 | CO_8420002516656263024478298 | 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"
]
| 2023-12-05T20:21:09 | 8424368164285988029574636956426971052596186588753901036806637944137212680118592157474288488550823133297528334071951925935572487761743595056499170439140592 | CO_8424368164285988029574636 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
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| [
3
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| true | null | null | -310,585.631234 | null | 0.992964 | 4.049547 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:43:53 | 8564260589621709620986161796026736297913292076729624585467184304319588552672986950217749910970942057996268869519695055074020541516197106728732439676271617 | PO_8564260589621709620986161 | {"config-type": "iter_6", "masses": [121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "domain_iter", "hash": "10777048120809009227309672395765308208465436897299362160231714591812677073762634094035286335286536364985928043125856410500708598918713462060854864646473292", "id": "MD_1077704812080900922730967"} | MD_1077704812080900922730967 | null | [
"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:08 | 8425129040079746463931517781134271761131365519162075586786755142078535558147760519738772439117442886029373870683692391029398263680345246550513845944235487 | CO_8425129040079746463931517 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
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1
]
]
| [
3
]
| 2023 | [
0.23630478379677952,
0.21855465772221375,
0.5451405584810067
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
Ge20Sb82Te144 | Ge10Sb41Te72 | A72B41C10 | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| 2023-12-05T20:21:06 | 8487152259008247621455933711406811659094243648258878901419647075523122922396204841155997413145226845052714605924921346417249749055142764993576825961009174 | CO_8487152259008247621455933 | GST_GAP_22_main | [
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"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| true | null | null | -189,480.871995 | null | 0.571261 | 1.305628 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:37:47 | 484334705131384226210006637373593703103492731945597112485237030708771491031024288943355728270635118355047651229811367426421875986334223457747916725374951 | PO_4843347051313842262100066 | {"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, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 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], "sub-config": "spc_hex", "hash": "4849465936302868063207162086697408080084481861245939143826428979056038334202629119467156448264109850464082774563506243465960103728109761004678584889493250", "id": "MD_4849465936302868063207162"} | MD_4849465936302868063207162 | null | [
"main_gst_gap_22_pbesol_cryst_spc_hex",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_hex",
"refitted_gst_gap_22_pbe_cryst_spc_hex"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 8496954361674455004859503022523541396283642008652148823236213488327168277941569310890233126114939173559460214032506122252324903748832591131680362101542581 | CO_8496954361674455004859503 | 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 |
Ge16Sb64Te112 | GeSb4Te7 | A7B4C | [
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| true | null | null | -256,739.583956 | null | 0.78508 | 2.76708 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:21:19 | 4711922235234396577900736914768638725383414894779401616789592661713136278903233305104381972836111278995958314073046555163397184033027127055879515733481353 | PO_4711922235234396577900736 | {"config-type": "iter_4", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "domain_iter", "hash": "11952051904056172062800397105971575375931239101489226403368625451737384110189775248661959008831571108647685249549995806430551849408451212636645228346232870", "id": "MD_1195205190405617206280039"} | MD_1195205190405617206280039 | null | [
"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 8497895597457241247620556484900578893866192274959999905749636113470153174346464316762224114397191976647666272771401245679454842671227295533793935265584784 | CO_8497895597457241247620556 | 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,
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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| true | null | null | -284,224.67865 | null | 0.906046 | 3.153225 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:04:44 | 1627120581380919882259781466237192888471615470906666759976767751845422754514357217676936810341257514721099201506362627952937516076956960233788054220444054 | PO_1627120581380919882259781 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 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", "hash": "7551892920841746894557062479756784129877252267805286331932417840503875924101937104337676838811473018013449796235830493817952249664374405465429366376270122", "id": "MD_7551892920841746894557062"} | MD_7551892920841746894557062 | null | [
"main_gst_gap_22_pbesol_cryst_spc_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs",
"refitted_gst_gap_22_pbe_cryst_spc_rs"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 850550821221431450836914860667383123341816786941711352942056622171115063187033141127274553749520585293977224228480660506251078709885027845878689897535941 | CO_8505508212214314508369148 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
1,
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1
]
]
| [
3
]
| 2023 | [
0.23630478379677952,
0.21855465772221375,
0.5451405584810067
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| [
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| 2023-12-05T20:21:09 | 850993153492303646169501186412073669472145298546460251987101201772308601451858015372210991237016853132644639880996489993152946175525284328629747882001077 | CO_8509931534923036461695011 | 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 |
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| true | null | null | -33,352.369594 | null | 0.164884 | 0.371351 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:03:03 | 6304782821747776143348484636104503144843870185434037333511328940953504780900978086921101869515112458290037547369385872591675613146658387880559001834171149 | PO_6304782821747776143348484 | {"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], "sub-config": "MP", "hash": "10679058983555083959294192798926178641315227263326678974785883751757917520483840727658159211821734554861957856287191262649912318382677885076471322262691530", "id": "MD_1067905898355508395929419"} | MD_1067905898355508395929419 | null | [
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| [
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| 2023-12-05T20:21:07 | 8529450496804677992633401182304048219091419074406754300425226537462916646879601831865274190715533905430394015355576112213595617587329250420108024141128293 | CO_8529450496804677992633401 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
GeTe | GeTe | AB | [
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| [
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| 3 | 5278442273367799440868511053328893276478065603819706443810604899683203702511152262336469366377727844618188325968225952097817186028381085303383446108187755 | 1 | CASTEP | PBEsol | null | [
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| true | null | null | -3,106.231394 | null | 1.562553 | 1.562553 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:35:30 | 3528747951449005889967179613275691662984004149338080189424090564167166469024919880997402302638363173009491033679452473736633161649232193721574463746322640 | PO_3528747951449005889967179 | {"config-type": "cryst", "masses": [72.63, 127.6], "sub-config": "rhom_GeTe", "hash": "6240288368831179278655173368742685790632375324962132005821718706446864372084663405911025033538700741186128949108475768232718859484959809394658838888233988", "id": "MD_6240288368831179278655173"} | MD_6240288368831179278655173 | null | [
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"refitted_gst_gap_22_pbe_cryst_rhom_GeTe"
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| [
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| 2023-12-05T20:21:03 | 8544782021077152429724109363875431759579766181222020908801191256129839229948722543475652429971892606094843188136698736635316445895478949863151113793269322 | CO_8544782021077152429724109 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
[
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1
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| [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| true | null | null | -77,234.039032 | null | 1.507399 | 4.582767 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:50:37 | 7591743239798025248454721683655745350108147273318182603556105320579048330798569156560621270846269097952618559549338072347331887869336312841040324976914770 | PO_7591743239798025248454721 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "conv_rs_anti", "hash": "10430917102308429461510551410938352132224254473219674085803862430256569190219123570344754167275189643033941325669449615678788421186337821511218241471469652", "id": "MD_1043091710230842946151055"} | MD_1043091710230842946151055 | null | [
"main_gst_gap_22_pbesol_cryst_conv_rs_anti",
"extended_gst_gap_22_for_efield_pbesol_cryst_conv_rs_anti",
"refitted_gst_gap_22_pbe_cryst_conv_rs_anti"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:05 | 8550150615109492727134349659571038883171218985093780848760099341413713202504638875955152436689903746760353793117210182565221205703698833772605358539221834 | CO_8550150615109492727134349 | 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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| 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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"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
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| [
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"DS_jy3ylaf48xg3_0",
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| 2023-12-05T20:21:04 | 8615457188672221931312257760562083566804448117905356043932160853923823020857134611733023794518117981971519614884095317131019508486201355622883386427746979 | CO_8615457188672221931312257 | 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 | -30,701.212846 | null | 2.523255 | 9.735858 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:30:17 | 1350897450469158693995257456403908130487905603864128484383437803838320788202604851999675869277152775279202408717564683605003506338008231794629140690563310 | PO_1350897450469158693995257 | {"config-type": "cryst", "masses": [127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "spc_ele_cryst", "hash": "10482341115092695104624036464431580998332388409744383351399034763727849966709074052587122925035349033589465704752437318101099927297656895760288257555499527", "id": "MD_1048234111509269510462403"} | MD_1048234111509269510462403 | null | [
"main_gst_gap_22_pbesol_cryst_spc_ele_cryst",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_ele_cryst",
"refitted_gst_gap_22_pbe_cryst_spc_ele_cryst"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 8621570635343738892955638689868424306816092722019770163585349657150397444547005707590116869069886257195758376340603440000603586131044066504231712098243210 | CO_8621570635343738892955638 | 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 | [
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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 |
Ge57Sb58Te144 | Ge57Sb58Te144 | A144B58C57 | [
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"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 8667189285502526923683152910700511965254591181659584414992389164510801714737303110064379270781638365642037110055702452466827522685244127876985118643411919 | CO_8667189285502526923683152 | 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_3_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_3_standard_iter",
"refitted_gst_gap_22_pbe_iter_3_standard_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 8670179409309190464033858751420962440911129550851270698467531723155549486375084726723078848573217231110991442788594761658697468567847883062703128799973186 | CO_8670179409309190464033858 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| true | null | null | -114,147.358522 | null | 1.004518 | 2.262665 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:30:01 | 6063890822289264290360234598950439317395971595157845159917168582547072650904003306429424835005681713405552582809700817216184933429127404469241016886551465 | PO_6063890822289264290360234 | {"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, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 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], "sub-config": "spc_hex", "hash": "1833062707632126152463528663644268874658489497539662541298359284453402049046387359673816135538780238189376492911875228133460742262582510363923115394402790", "id": "MD_1833062707632126152463528"} | MD_1833062707632126152463528 | null | [
"main_gst_gap_22_pbesol_cryst_spc_hex",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_hex",
"refitted_gst_gap_22_pbe_cryst_spc_hex"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 8687767693974475220321366650376327952848472594605654765474430820381332424637388662973919136758016567759716122153104939545561918020186684153767537171075730 | CO_8687767693974475220321366 | 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,
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 |
Ge57Sb58Te144 | Ge57Sb58Te144 | A144B58C57 | [
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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 | 8690136131500763630220674117873234815954333975000224355437619498948512675223037554232630179295826158457429877979768011198153118000220272857395290528590474 | CO_8690136131500763630220674 | 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 | 8695681457973626525404382144011001814426163031373014098723369726065580359956173302802410855132545061001039161734778891903921380469762858111689084545290269 | CO_8695681457973626525404382 | 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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"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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"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 | 8746704201698380529844572359068972951817256209363108637168015550755726751856711184755632736381491992569736976564153121638669382977794601478003143960035143 | CO_8746704201698380529844572 | 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 |
Ge20Sb82Te144 | Ge10Sb41Te72 | A72B41C10 | [
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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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"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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"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 | 8823043027235634731836372053462141937482462050535199100516217895007772408049698779201921239384140675910996969366833367986061819638873615099999882638864082 | CO_8823043027235634731836372 | 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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"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 | 8827019337307842189760034611425465096445281842103892607768201278162498517244250812978732820892985047749341615939595242085145892971811407905597512721155381 | CO_8827019337307842189760034 | 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_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 | 8827522693219131639895752953349750829157387317341612723680367088888162756607612573236601945013096498344519971322062839508941137648763550505548625375298003 | CO_8827522693219131639895752 | 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 |
Te | Te | A | [
52
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| [
"Te"
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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_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 | 8941463930449707830597468926003359305235421670317815784177118553754257216199984110444458227030772450315361124582265594544777163576279527214170550840822457 | CO_8941463930449707830597468 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"main_gst_gap_22_pbesol_iter_1_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_1_standard_iter",
"refitted_gst_gap_22_pbe_iter_1_standard_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 8954534813753568618105142219621549871565855606503300011274812460227666933927776386155686763417976136867192697598216842340922056446918581143835329889792197 | CO_8954534813753568618105142 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| true | null | null | -313,184.290307 | null | 0.649869 | 1.906871 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:34:35 | 9502872090085435316981255426870794037329904015835002450756106811248093526769897627781552737972463145772442665454049796806052911239234550154569325257230023 | PO_9502872090085435316981255 | {"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, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 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, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 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": "9142202894591766066263745136223938419302794196867161202648447471128761387654600054566912928545273297883778853753745598099051563957984109928081731120293937", "id": "MD_9142202894591766066263745"} | MD_9142202894591766066263745 | 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"
]
| 2023-12-05T20:21:06 | 8968426278859728366504260721518073751837592086862135345214121633868339783304390874790714924713111492929253558330732982890133892504878127808497462641604549 | CO_8968426278859728366504260 | 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,
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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| true | null | null | -313,189.886766 | null | 0.622489 | 1.778616 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:35:05 | 10386212515897284495345756776989178820307035719450537175686481625277019639233269093828215734585025770278571476227055385114054146669326951832929484307032427 | PO_1038621251589728449534575 | {"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, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 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, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 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": "9142202894591766066263745136223938419302794196867161202648447471128761387654600054566912928545273297883778853753745598099051563957984109928081731120293937", "id": "MD_9142202894591766066263745"} | MD_9142202894591766066263745 | 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"
]
| 2023-12-05T20:21:06 | 8982066919542070062379768836947406588742523145796591649996671020558305737687590063792645541081592095759581846033816466137856152015240806209054215819475190 | CO_8982066919542070062379768 | 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 |
Ge20Sb82Te144 | Ge10Sb41Te72 | A72B41C10 | [
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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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| 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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| 2023-12-05T20:21:04 | 9000171296753827939808219708544696675136508456903087804137355444538514947563418976113042373713729025046517316366850833242551764889835351286981875863272004 | CO_9000171296753827939808219 | GST_GAP_22_main | [
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"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 9033708713186626856912229516867703330128326509479148076782869732060854612509378694626689927731789455750268185415312990272743664484250165466538478008410766 | CO_9033708713186626856912229 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"main_gst_gap_22_pbesol_iter_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 | 9090336636337251701501784553736513136443285003646893471515254772976212193247185765355421497269415946291121753798979361395073358096430429598778055284111576 | CO_9090336636337251701501784 | 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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"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 | 9091760712705556425308685591178385614201416444226425582277565311903726793710439411719602281413119804188795167936786793611351218623209465717420422694699729 | CO_9091760712705556425308685 | 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 | -245,898.306637 | null | 0.533066 | 1.399555 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:56:48 | 11972987595437873213048743124395566810636292134272207496726909006863062140639588378119671939809588016566136667348189109688323357862369010336143415998452683 | PO_1197298759543787321304874 | {"config-type": "iter_2", "masses": [121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "standard_iter", "hash": "4415168539254758589942931451969303721115222292080397924978179195697049209994957843001071003049866381675010346314412424454152226742193272429914947749831800", "id": "MD_4415168539254758589942931"} | MD_4415168539254758589942931 | 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 | 9105076745318383180879091123229570888533464861849773423208710640686471790191026661900184959654623423954901202301267755533406846961312788066121432826771511 | CO_9105076745318383180879091 | 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,
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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| 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 | -114,357.376138 | null | 0.57958 | 0.5796 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:59:51 | 11948482829973384782236144907527914059432264663305438197564027126339554727778807566453759377682299342273560961340544533930976374738024409010737091925454379 | PO_1194848282997338478223614 | {"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"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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| 2023-12-05T20:21:04 | 9211204464620993322118442264876699292279500004078225944652545279216067481722862961064679510422113266644397134381331692554183916575850548673018755343079939 | CO_9211204464620993322118442 | 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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| 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 | -232,969.988027 | null | 1.00575 | 2.417762 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:53:19 | 9085781072373472706719736752935972056353442815221474958581165330776698963177848025678638345506260993426812683238070295369249663386175656972309847739487031 | PO_9085781072373472706719736 | {"config-type": "cryst", "masses": [121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "spc_rs", "hash": "54678195205937786065740927994264775129473293687619054681040553800957511764679013983335221251307539950855477746420713185268883002559195409602649142313830", "id": "MD_5467819520593778606574092"} | MD_5467819520593778606574092 | null | [
"main_gst_gap_22_pbesol_cryst_spc_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs",
"refitted_gst_gap_22_pbe_cryst_spc_rs"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 9325037076084623096891549801992202725113235417410269456396180492612949869575840738893518246740708988689879669107039210664659394584039779914620101561694844 | CO_9325037076084623096891549 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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3
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| true | null | null | -275,376.801171 | null | 0.933753 | 2.406777 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:54:45 | 7707094398767347363689015957653395908144148650807309848813122591510061799249642455495559005942672122028424103390849643513965568232246453750723431692710763 | PO_7707094398767347363689015 | {"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", "hash": "5262879420142088628170560071264663034945162511930065106824126619983954991792965606257950473862846843983516830621207984898638852226857597003304264334439970", "id": "MD_5262879420142088628170560"} | MD_5262879420142088628170560 | null | [
"main_gst_gap_22_pbesol_cryst_spc_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs",
"refitted_gst_gap_22_pbe_cryst_spc_rs"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 935606874956372496545442541413634499613753049552629382258998133369458831043124250066747384444441731295680760265455033223025142879543328947301786269505809 | CO_9356068749563724965454425 | 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,
0.21855465772221375,
0.5451405584810067
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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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"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 | 9447120585740297636488680198286901631614345942633070421658862436636664400879663143263913633392225475301223266976779578797540543837067685464042369876474758 | CO_9447120585740297636488680 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 9490085813943193753117765809030744780515553514281041179864730562851644556082831548614196648997742709268037428490059764962373380581085726103007199980833257 | CO_9490085813943193753117765 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:07 | 9536720830355426763206783632938349624016260549297107969683633909868745888583732073401841085205270315308721722698576023996066756888658270233842764564038868 | CO_9536720830355426763206783 | 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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| 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 | -99,396.539602 | null | 1.028021 | 4.193816 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:08:27 | 3324515199871570874769033552576626990631269999778894833142818793477981535157177566658367657213429229290193672602228062970881679837404674861781741478712896 | PO_3324515199871570874769033 | {"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, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 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": "681716355135397353462237636742726527852563435178652437218498139193851703110440338177099677277320866428303316171599538877822007169223948173320969186870333", "id": "MD_6817163551353973534622376"} | MD_6817163551353973534622376 | null | [
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| [
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| 2023-12-05T20:21:06 | 9567246378697069032319685746961906830172232674172008159827902833702576826334991430645928605295552620262948564032558346175653331022562878165060496729608981 | CO_9567246378697069032319685 | 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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"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"
]
| 2023-12-05T20:21:07 | 9588569641940592029524706643918967283615354090311841749791393082802809017158657991624995451541294812070964952214023952462517966547671663789512563501799147 | CO_9588569641940592029524706 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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| true | null | null | -64,188.60457 | null | 1.919992 | 9.683686 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:23:52 | 2969237290651689729419711556722592386114703854657609496627808651312687368197540685720739303243513360452531219067174102817525780953561024779312473791510721 | PO_2969237290651689729419711 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "spc_hex", "hash": "4258300117514373197389689128291023977616299557097794243333972937154259204258235931656677942732644191457793616264176506159502295554843995743447397856776532", "id": "MD_4258300117514373197389689"} | MD_4258300117514373197389689 | null | [
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| [
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| 2023-12-05T20:21:05 | 9592003570154140305369313582912860577768535922312180316942631367726055000199812605061729411845012278511741272336014745921191470818195428771690067399343222 | CO_9592003570154140305369313 | 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",
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"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
GeTe | GeTe | AB | [
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52
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| [
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| true | null | null | -3,106.331296 | null | 1.16912 | 1.16912 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:19:34 | 5370481341747415497032165514390582032193731761435221160730560874150470579477038384277039371316298527149699286827293836715244322352407884598586077353096070 | PO_5370481341747415497032165 | {"config-type": "cryst", "masses": [72.63, 127.6], "sub-config": "rhom_GeTe", "hash": "6240288368831179278655173368742685790632375324962132005821718706446864372084663405911025033538700741186128949108475768232718859484959809394658838888233988", "id": "MD_6240288368831179278655173"} | MD_6240288368831179278655173 | null | [
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"refitted_gst_gap_22_pbe_cryst_rhom_GeTe"
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| [
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]
| 2023-12-05T20:21:03 | 9643110365781521176800344960039364403357026236116031084111756113748778459615839536949420830297728417063622642059050121547472627410030359361072934765179017 | CO_9643110365781521176800344 | 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 |
Ge2Sb2Te5 | Ge2Sb2Te5 | A5B2C2 | [
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| true | null | null | -12,681.995418 | null | 1.092392 | 1.49181 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:14:37 | 1903009660929259008167347824645710368757773476397693921205781312137838416042223561090932224443700433475103640234168923860433807610685224661908213219412480 | PO_1903009660929259008167347 | {"config-type": "cryst", "masses": [72.63, 72.63, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "conv_hex", "hash": "3736747359295474668976008674728693682234744152525329248995556077271058404753842235067000269318827100579604246377666184098831224454551029227862824894315935", "id": "MD_3736747359295474668976008"} | MD_3736747359295474668976008 | null | [
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| [
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]
| 2023-12-05T20:21:04 | 9649775099240390068551855474536171914050564653852243630854958626249255493815322426458601712558909266108474955066696445878518532497411465090279417671512717 | CO_9649775099240390068551855 | 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 |
Sb24Te36 | Sb2Te3 | A3B2 | [
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| true | null | null | -77,645.62599 | null | 2.757378 | 12.050264 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:53:57 | 4609683311434402663786610223155062479831707798723764014208985587957508462358418247617649229925092492503719022746941991969661245692430597000544955199331488 | PO_4609683311434402663786610 | {"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, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 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": "6270198133287809757984285892781492672025869913481832769729666009949457288351394447555527832505007715727449526544276552533269880409801694904811629803048351", "id": "MD_6270198133287809757984285"} | MD_6270198133287809757984285 | null | [
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"extended_gst_gap_22_for_efield_pbesol_cryst_spc_hex",
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| [
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"DS_jy3ylaf48xg3_0",
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| 2023-12-05T20:21:05 | 9656663186807079442701352416819492353756478842589699554521401840766377645052229051548295136558750377171616201219608887661344169358970211062261611775746483 | CO_9656663186807079442701352 | 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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| [
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| 2023-12-05T20:21:05 | 9660230075893425520155112094763384680762041266984110438130050873654614712844776072214009506167473653349064961017854134326190884255805775240061728523003874 | CO_9660230075893425520155112 | GST_GAP_22_main | [
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"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
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| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| true | null | null | -16,049.39468 | null | 1.143635 | 1.840403 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:46:27 | 4447187351437299016513375541025815033382568576710228764583656371734950799638794371723754187173440548045788093402167327440824996325844941853846082535039639 | PO_4447187351437299016513375 | {"config-type": "cryst", "masses": [72.63, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "conv_hex", "hash": "11606914213497341411008595714685281519729200534343730250725059374261891914615845843530564979381996516452893744129228585192303421822938486955566716032194708", "id": "MD_1160691421349734141100859"} | MD_1160691421349734141100859 | null | [
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| [
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| 2023-12-05T20:21:04 | 966603205590336584768936827792088203815557585245590248079629027248919549326887317819021105728443050613731039274471516581823150696691976342402024793469112 | CO_9666032055903365847689368 | GST_GAP_22_main | [
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"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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GeTe | GeTe | AB | [
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| true | null | null | -3,106.424422 | null | 0.191619 | 0.191619 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:07:59 | 3449065905248039531559738181393349693337099709528621981236327348194674346810330822644672447129326379123241923778864167701417638573449510997023262397610442 | PO_3449065905248039531559738 | {"config-type": "cryst", "masses": [72.63, 127.6], "sub-config": "rhom_GeTe", "hash": "6240288368831179278655173368742685790632375324962132005821718706446864372084663405911025033538700741186128949108475768232718859484959809394658838888233988", "id": "MD_6240288368831179278655173"} | MD_6240288368831179278655173 | null | [
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| [
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| 2023-12-05T20:21:03 | 9671593817807105822081204132841039564256007723340992918973509920823646975401172258679092185808389442095836669312551180620089643348915136111594496338235898 | CO_9671593817807105822081204 | GST_GAP_22_main | [
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"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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"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"
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| 2023-12-05T20:21:05 | 9692955943361745174111274176290879036789309351830204422820792566462539187475244782435079533281253546892038335021297426062947816506469581145038112205214541 | CO_9692955943361745174111274 | 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 | -247,207.684685 | null | 0.969709 | 3.505149 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:38:01 | 7502025243324133566246763222909800995845902786024262054360119041211195584768345564260323456408924165239264875139803292443154444132568607713450593310337514 | PO_7502025243324133566246763 | {"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", "hash": "1064850008900002283554905325065988711945428462166081903155290998480116044087764571442801629261300445737859793008949549629015232965429419635052304003855145", "id": "MD_1064850008900002283554905"} | MD_1064850008900002283554905 | null | [
"main_gst_gap_22_pbesol_cryst_spc_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs",
"refitted_gst_gap_22_pbe_cryst_spc_rs"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:06 | 9702254319100954981160328555784438353292389712892918906983497147960922920028823985596689700522301787000431764125255082296771474249839622103191523949851236 | CO_9702254319100954981160328 | GST_GAP_22_main | [
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
]
| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
"Ge",
"Sb",
"Te"
]
| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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| 2023 | [
0.23630478379677952,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None} | 10.60732/f2d6e02c | 826502182058204038828462580985484461807581576654269362365982313471505999510032901974865453994191390896192775663922834854548492346881903106954850344893615 | DS_r3hav37ufnmb_0 | GST_GAP_22_main__Zhou-Zhang-Ma-Deringer__DS_r3hav37ufnmb_0 |
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"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
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| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:08 | 9722589380705985052072516997224111666981838844406285108803168351417473218550257050088106309242267411566663345599863271096395524061369216060850862383620593 | CO_9722589380705985052072516 | 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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"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",
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| 2023-12-05T20:21:04 | 9756192546823290265478954250323020845008628556351021520538640047201332807519880376842162631163679723687884833555954902740998973267059881645354515832019310 | CO_9756192546823290265478954 | 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 |
GeTe | GeTe | AB | [
32,
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| [
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| [
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| 2023-12-05T20:21:03 | 9860747763712298810419909051033441930995231250670351289580272581434189072024998533531581887099587008948488380335754873642946042905883682636784537701932773 | CO_9860747763712298810419909 | GST_GAP_22_main | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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Te2 | Te | A | [
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| [
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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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GeSb4Te7 | GeSb4Te7 | A7B4C | [
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| [
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| 2023-12-05T20:21:04 | 9907108030117704523186801700321499822812687981426007475763871395410149442810555325919633753581485412741482976022015441020062838315579995147956927815298423 | CO_9907108030117704523186801 | 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"
]
| 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 |
SbTe | SbTe | AB | [
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| null | null | null | -2,943.870339 | null | 0.41636 | 0.41636 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:31:24 | 12020784526840231110929628453348495196989026046540899836654653780327881171903482681537454979426829180516810649156418220317455335394174776570547423085344450 | PO_1202078452684023111092962 | {"config-type": "dimer", "masses": [121.76, 127.6], "sub-config": "dimer", "hash": "6515008115902207792427764327069119881771508369463765552696522573764978900341385467272551511968839744382490372532353233233123211419700549454455532510838416", "id": "MD_6515008115902207792427764"} | MD_6515008115902207792427764 | null | [
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| 2023-12-05T20:21:03 | 9937342943553391685155887761541883570601989269560905165513412542040467741468311494711102296322201956585989734297125511170753588944863310292577683305829289 | CO_9937342943553391685155887 | GST_GAP_22_main | [
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| The main training dataset for GST_GAP_22, calculated using the PBEsol functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions. | [
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| 3 | 2,692 | 2,692 | 341,068 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,244.404802 | 20,950,469,103.777985 | 0 | 2023-12-05T15:20:14 | [
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"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 | 9946151890673710323699177880576864912272094445936352149851439413452093743848563365750217642122800413339522916979727681039460007209221680569391170855339021 | CO_9946151890673710323699177 | 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 | -83,865.859472 | null | 1.036913 | 3.449106 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:10:28 | 4478705699418500004295562589544790582500963092455107741268345927083762084987499924402100168994699277029100299336143435611774524205998500623966619476554741 | PO_4478705699418500004295562 | {"config-type": "cryst", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "spc_hex", "hash": "13235791543569574279385427345369658452407681141389265953886517022488712993886195517946376035610230732619538486345132643668941183458936963654831828295697861", "id": "MD_1323579154356957427938542"} | MD_1323579154356957427938542 | null | [
"main_gst_gap_22_pbesol_cryst_spc_hex",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_hex",
"refitted_gst_gap_22_pbe_cryst_spc_hex"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 9957487524861255309379868388378830391476926983237724990551273828752713961917162885933170584994869504646548358823410528318134279258584437088030206047778780 | CO_9957487524861255309379868 | 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"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:05 | 9964060046755958834508080811598532092300637440284053802445202614820006249399303885999672813452895607193699435746340522950266136580278626502361798554657934 | CO_9964060046755958834508080 | 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 | [
51,
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52,
52,
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| [
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| [
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[
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[
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| true | null | null | -19,414.295155 | null | 1.367599 | 2.893831 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T15:22:29 | 11076119837730936680309333664162593430682976990442129521326428161582308887355428799029732528113531709614302366250365906559332074094948111917840100418140975 | PO_1107611983773093668030933 | {"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"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 9968410168261470854578260460151921454251144248878459657950766245505120268787910876290373837801843482085684767333725214086941316296450063575003027004229193 | CO_9968410168261470854578260 | 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 |
Sb6 | Sb | A | [
51,
51,
51,
51,
51,
51
]
| [
"Sb"
]
| [
1
]
| 1 | 6 | [
[
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0
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[
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0
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[
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[
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[
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| [
true,
true,
true
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| [
1,
1,
1
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| 3 | 3057861243091442703305109968882834722675541212002121274259736869607935164754427989972570641378076861639849793383524266726781823555934551222337340244408605 | 1 | CASTEP | PBEsol | null | [
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[
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| null | [
[
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[
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| true | null | null | -14,295.200273 | null | 0.685565 | 0.68562 | {"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"} | MD_2127439833971556448789806 | 2024-08-16T14:27:11 | 1416280037811661445272019399370399861977081873435335434646355279611771520999133253400287191526255405346993681829296812167903620006116177316055538190624237 | PO_1416280037811661445272019 | {"config-type": "cryst", "masses": [121.76, 121.76, 121.76, 121.76, 121.76, 121.76], "sub-config": "conv_ele_cryst", "hash": "11376588209332849124881644127347225818768470654784049131706689891424440782678255600298876432210759186988575066593177855023956768212279145568357977314864761", "id": "MD_1137658820933284912488164"} | MD_1137658820933284912488164 | null | [
"main_gst_gap_22_pbesol_cryst_conv_ele_cryst",
"extended_gst_gap_22_for_efield_pbesol_cryst_conv_ele_cryst",
"refitted_gst_gap_22_pbe_cryst_conv_ele_cryst"
]
| [
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
]
| 2023-12-05T20:21:04 | 9971770630895550432553538017887838776650083512506617049937524589629365516683511876249963162944319772871359918837501666682500365895058474215414880599977200 | CO_9971770630895550432553538 | 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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