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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Al2SbSi6Sn | Al2SbSi6Sn | A6B2CD | [
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| [
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| null | null | null | null | 13.646992 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T15:39:51 | 5208511731427564696106122753672501714537535238171851242274483481331410058111645265246788015197393451976743933352872480161646586723115324033849114551890383 | PO_5208511731427564696106122 | null | null | null | [
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| 2023-12-01T23:04:56 | 9283246825992661200834580940452961827199253859003668447690316621383369370033860930261841046399412170613900559085989451914044192275187799877643253790798478 | CO_9283246825992661200834580 | ABC2D6-16_PRL_2018 | [
"Felix Faber",
"Alexander Lindmaa",
"O. Anatole von Lilienfeld",
"Rickard Armiento"
]
| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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]
| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
C2AsSbSi6 | AsC2SbSi6 | A6B2CD | [
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14,
14
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| [
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| null | null | null | null | 24.385904 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T14:28:49 | 2971403601242397177454909842811782477516287157284435589144136462264735351458199828523470979034340538033349776336824832996587314076664954357457952249585494 | PO_2971403601242397177454909 | null | null | null | [
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| 2023-12-01T23:04:56 | 9285215746964629802687989405553827524814902556093971480713655661372509223759119975201227969769944029573301796261161056502312172675296989499593644271063290 | CO_9285215746964629802687989 | ABC2D6-16_PRL_2018 | [
"Felix Faber",
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"Rickard Armiento"
]
| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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]
| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
Ga6NSb2Sn | Ga6NSb2Sn | A6B2CD | [
51,
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50,
31,
31,
31,
31,
31,
31,
7
]
| [
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| [
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| null | null | null | null | 2.682491 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T14:33:29 | 12640922508429571394225694874157167942471642876849055875596941778043380479697101492734357332828415365432998664377947661577478531949548257071684225793780650 | PO_1264092250842957139422569 | null | null | null | [
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| 2023-12-01T23:04:57 | 9288746694910709046817486623229831587584728342264345066886451582253687528848244403713345239834337168242292121460108926845845042424282172215651025720777807 | CO_9288746694910709046817486 | ABC2D6-16_PRL_2018 | [
"Felix Faber",
"Alexander Lindmaa",
"O. Anatole von Lilienfeld",
"Rickard Armiento"
]
| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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]
| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
AlGe6NeTl2 | AlGe6NeTl2 | A6B2CD | [
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81,
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32,
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32,
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32,
13
]
| [
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| null | null | null | null | 7.25692 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T15:13:25 | 10818061843625281305712193368308521993581918805438620117489029189250328861357892861109627046521117419871615410116133824703666963931127717141314723082627008 | PO_1081806184362528130571219 | null | null | null | [
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| 2023-12-01T23:04:57 | 9291548808117849673976885022432639753139277164276226171139572997353825092338533660019563660187047731882228335999667462642549552833180333318738837456548759 | CO_9291548808117849673976885 | ABC2D6-16_PRL_2018 | [
"Felix Faber",
"Alexander Lindmaa",
"O. Anatole von Lilienfeld",
"Rickard Armiento"
]
| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
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| 2023-12-01T23:04:57 | 9355113342035526453144867767484195189070030184362829075040027856995473409062973383571133716003654548285587922284714923452269353627778280772431387639899935 | CO_9355113342035526453144867 | ABC2D6-16_PRL_2018 | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
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| 2023-12-01T23:04:56 | 9364204793046997076439770242567696884234726326893090584346832345690095958372848550806379319596307503211647872180207440390066758187180018796788378348132278 | CO_9364204793046997076439770 | ABC2D6-16_PRL_2018 | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
AlGaP6Sb2 | AlGaP6Sb2 | A6B2CD | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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CAl2NP6 | Al2CNP6 | A6B2CD | [
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| 2023-12-01T23:04:56 | 9388100912852176284740585263251004112997448838333379359497871339860579811784818388970154483494967568803584937193078261606016766436440982658416798827329755 | CO_9388100912852176284740585 | ABC2D6-16_PRL_2018 | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| 2023-12-01T23:04:57 | 9399539979988292904309902968688494251141757226731078610478565688993664302448210500058100849227526241115955216566860736794006696879466360608203571386072148 | CO_9399539979988292904309902 | ABC2D6-16_PRL_2018 | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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]
| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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"Felix Faber",
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]
| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
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| null | null | null | null | 18.107474 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T14:48:54 | 6698261134088354318185178151386632018841799720643040751633338443303185833350903248141102381319672436038302434401716341930614997854759785827134202247698962 | PO_6698261134088354318185178 | null | null | null | [
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| 2023-12-01T23:04:56 | 94394841630769452109808159077296775420184240867746915237843806085768974546169943167023070900595177221531062673370296328626090154003496521132851808899221 | CO_9439484163076945210980815 | ABC2D6-16_PRL_2018 | [
"Felix Faber",
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]
| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
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| null | null | null | null | 29.185103 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T14:24:51 | 11648846711948677890184450207634681122029196454431821902756511032973012460590679333938421483289673757368066979426846599336078415272126152439415352499020258 | PO_1164884671194867789018445 | null | null | null | [
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| 2023-12-01T23:04:57 | 944246907729465009428445727699194586668869524258941965421270807900378331430417628045401420387461033143307133478233555137122722136591466888011762507763285 | CO_9442469077294650094284457 | ABC2D6-16_PRL_2018 | [
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]
| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
AsO2S6Si | AsO2S6Si | A6B2CD | [
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| null | null | null | null | 10.477344 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T15:38:05 | 8247462382138217957127713333944739897497588385984160698131039089095569827457429805435446012527868975750337706669293232904150414327494323667220822135685194 | PO_8247462382138217957127713 | null | null | null | [
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| 2023-12-01T23:04:57 | 9445993534685147987743395970057504539499997755255482450338304940521793386028836532273413281772870121727126818794929081036795710536015405925906715322549752 | CO_9445993534685147987743395 | ABC2D6-16_PRL_2018 | [
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"Rickard Armiento"
]
| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
AsBiF2Pb6 | AsBiF2Pb6 | A6B2CD | [
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| [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 2023-12-01T23:04:56 | 9513403623876038068539628777632120003332860135406056021739497610017743639425508459567979533717275600959979334497421748329746695272701842928650936747396211 | CO_9513403623876038068539628 | ABC2D6-16_PRL_2018 | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
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| 2023-12-01T23:04:57 | 951735369303320345311302294481681012340653213551624840976183816649946537193026611147550656852132161229358297425878523487494370587768512238909140065071153 | CO_9517353693033203453113022 | ABC2D6-16_PRL_2018 | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| 2023-12-01T23:04:56 | 9525195383774025770834034848229871563233887630488052678154556173841133409922608408856232566339633885302908553810623774200152226759781418456265320770570021 | CO_9525195383774025770834034 | ABC2D6-16_PRL_2018 | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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BrIn6N2Xe | BrIn6N2Xe | A6B2CD | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
Ge2HeI6Si | Ge2HeI6Si | A6B2CD | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
CBiCl2S6 | BiCCl2S6 | A6B2CD | [
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]
| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
CBe2LiO6 | Be2CLiO6 | A6B2CD | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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AsGeN2Sb6 | AsGeN2Sb6 | A6B2CD | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| 2023-12-01T23:04:57 | 9577203465487769218066645019267884863186689576600497328025676806150908693648809024269216670777311051903344124526384878571138201341677999856978593785236339 | CO_9577203465487769218066645 | ABC2D6-16_PRL_2018 | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 2023-12-01T23:04:57 | 9583848158083564225454813417958147153090894693723976997634348582908044246317911913266373441799511041912156937130187005639918639360442834195964600689300041 | CO_9583848158083564225454813 | ABC2D6-16_PRL_2018 | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 2023-12-01T23:04:57 | 9594993683226035729369652890455457728352740504708594077052228147723175977680895260170233658501212813037208825950131437662078215869170496330694128299637547 | CO_9594993683226035729369652 | ABC2D6-16_PRL_2018 | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
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| 2023-12-01T23:04:56 | 9596918389346648369343020110755330837846523810346227637095897771642500338282080458843389666071943117764548797791424278796000279101690462064212292126778406 | CO_9596918389346648369343020 | ABC2D6-16_PRL_2018 | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| 2023-12-01T23:04:56 | 9598077666189363740033964299379325990611636453527274750468098005102428840139185106851726677582689422949404324302062748201577049280550243209145944496979800 | CO_9598077666189363740033964 | ABC2D6-16_PRL_2018 | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 2023-12-01T23:04:57 | 9606575881131914442813607251728152425558570292825732047341995205238342679752296085981720018873972023171857191143440721202889068032990370372545987494256684 | CO_9606575881131914442813607 | ABC2D6-16_PRL_2018 | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
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| 2023-12-01T23:04:56 | 961035440677802419848280583450229452873281455012766057611845657418622272010948028409499226545840966963777194034321870995865489631230822005127661049512146 | CO_9610354406778024198482805 | ABC2D6-16_PRL_2018 | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
C6GaPSi2 | C6GaPSi2 | A6B2CD | [
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| 2023-12-01T23:04:56 | 9636919233000881597325606729933069932801546037439328571814693122646982456027259636537895501425295253401927467383730113947229234247338125104131177124764105 | CO_9636919233000881597325606 | ABC2D6-16_PRL_2018 | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| 39 | 21,881 | 21,882 | 218,820 | 0 | 21,881 | 0 | 21,881 | 0 | 0 | null | null | 21,881 | 2023-12-01T18:05:52 | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). | [
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| CC-BY-3.0 | {'source-publication': 'https://doi.org/10.1103/PhysRevLett.117.135502', 'source-data': 'https://qmml.org/datasets.html', 'other': None} | 10.60732/f87a64e4 | 12110994724639911956047482088690986156528073902140895356413132972271341337895444635192517682239989058361394442535062960949949218512917835653997173189061933 | DS_0ady7a8a8n6p_0 | ABC2D6-16_PRL_2018__Faber-Lindmaa-Lilienfeld-Armiento__DS_0ady7a8a8n6p_0 |
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