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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Al2N6PS | Al2N6PS | A6B2CD | [
15,
16,
13,
13,
7,
7,
7,
7,
7,
7
]
| [
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| [
0.2,
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[
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[
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]
| 2023-12-01T23:04:56 | 9652405788003142614504654506827472854880580250220165803522172195495975833560043771327841056493698012879190974697333971053623395826401078429202788186039330 | CO_9652405788003142614504654 | 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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"Te",
"Bi"
]
| 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 |
Ca2KS6Se | Ca2KS6Se | A6B2CD | [
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20,
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34
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| [
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| [
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| 2023-12-01T23:04:57 | 9660936641848210235598782413339004478556875144758355868070777891812501889159058097882645273774052767335865983038541273924153559113883173445649519434335755 | CO_9660936641848210235598782 | 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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"Bi"
]
| 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 |
C6AlN2S | AlC6N2S | A6B2CD | [
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6,
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13,
7,
7
]
| [
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| [
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| 4 | 10 | [
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| null | null | null | null | 32.766395 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T14:27:12 | 10892736508459465148121752345106447273486419066232001836283289085539158316468348888408026079760373473952180954951207665491842461546751442254999728944562020 | PO_1089273650845946514812175 | null | null | null | [
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]
| [
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| 2023-12-01T23:04:57 | 9678516102619892753314989707938800249612533125728069383335147511335201034242239781264512309236318053914479949625642251467798293360020663606669985222318329 | CO_9678516102619892753314989 | 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 |
CGeP6Si2 | CGeP6Si2 | A6B2CD | [
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15,
15,
15,
15,
15,
32,
14,
14
]
| [
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| null | null | null | null | 11.099475 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T14:44:45 | 1369170828449931336294318864753768482335479672644737212238341227757335400615868662888512968266719230630973880544439848293710593319224896734191518988172563 | PO_1369170828449931336294318 | null | null | null | [
"ElpasoliteIIItoVI_1941"
]
| [
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]
| 2023-12-01T23:04:56 | 9680329284808414180152214542751590931593880176817175762498368749824457379494618391782970762411217633437095600206772078558225146075165221391492534324802175 | CO_9680329284808414180152214 | 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). | [
"As",
"B",
"In",
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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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| 2023-12-01T23:04:57 | 9699158450750141489096553145148873985486041270378552314914036365773913081594328223434148624283005394432328169548236943482286947583623488464650247563977804 | CO_9699158450750141489096553 | 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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AsGa2SbSi6 | AsGa2SbSi6 | A6B2CD | [
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| 2023-12-01T23:04:56 | 9707912288783318848893168559142991246937952892100046917215253308079266003763604688963890815725343359102412470594496150203325280982467365355366078077575199 | CO_9707912288783318848893168 | 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 |
Al2AsO6Sn | Al2AsO6Sn | A6B2CD | [
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| [
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| 2023-12-01T23:04:57 | 9723230053894519897523780929721982737722104536543775929586133284785856872321654908616387381683125368088976192141373635522921368366276430612189712239709422 | CO_9723230053894519897523780 | 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 |
H2BeLiP6 | BeH2LiP6 | A6B2CD | [
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| null | null | null | null | 11.629999 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T14:59:31 | 6698159537983728896207722571757289074702559895801109400625308567869065015423648217212904992247434629319353301315070580638041996294615996527876281700993852 | PO_6698159537983728896207722 | null | null | null | [
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| 2023-12-01T23:04:56 | 9725903674594282231179928801651218269524942724498234904951217942714064251089557428771061209796800135992982702641091593322481319612532662669455582186406282 | CO_9725903674594282231179928 | 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 |
Al2BPb6Sb | Al2BPb6Sb | 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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49
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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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49
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| 2023-12-01T23:04:57 | 975471494720896163676499152505917664737725053658013811992767405546402893407063165485434540610614139740571382290454204055696335257177978761580909978772710 | CO_9754714947208961636764991 | 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 |
N2PSSb6 | N2PSSb6 | 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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| 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 |
Ar2B6BrTe | Ar2B6BrTe | 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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Bi2BrSeSn6 | Bi2BrSeSn6 | A6B2CD | [
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| 2023-12-01T23:04:57 | 9767241758660475697540428788716511359150568073563951126884081439426794486192325754174053915277695590331187804231430927464718567681125414563058760663407177 | CO_9767241758660475697540428 | 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 |
BiIS2Sb6 | BiIS2Sb6 | 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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| 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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| 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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| 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 | 9855388138866702817924109171551266870071174385624903009499194913598068995827310033174342265705391613468095672720428830715644000232989854549117549887210503 | CO_9855388138866702817924109 | 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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| 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 |
H6Ne2SSn | H6Ne2SSn | A6B2CD | [
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10
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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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| 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 |
As6GaOP2 | As6GaOP2 | A6B2CD | [
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31
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| [
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| 2023-12-01T23:04:56 | 9867100762507497695414243252597385516988115836868012727650125415808544456380159422751147766297036745804839947357072176565571720841182776094223669463739627 | CO_9867100762507497695414243 | 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 |
HB2Ga6S | B2Ga6HS | A6B2CD | [
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31
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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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| 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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| 2023-12-01T23:04:56 | 9897292351244774839366016855760901306115337970120881978764604161761908455646978085195530419252165059349410350417029299305828590917167030070334837956239603 | CO_9897292351244774839366016 | 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 | 9904940326571987853015174138315304466598655598722722947081943762333709423998086133009382102899739196174247248783142020188696629166140174795123573142458192 | CO_9904940326571987853015174 | 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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GeO6P2Sn | GeO6P2Sn | A6B2CD | [
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| 2023-12-01T23:04:56 | 9906733235741701501542448083233560529635014190465286836718181311667623070911229623140445331376703197762577386661346418540339376946721206804379532662256786 | CO_9906733235741701501542448 | 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 |
N6PSiSn2 | N6PSiSn2 | A6B2CD | [
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| [
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| 2023-12-01T23:04:56 | 9911065276032760076739005720185995315579220544039498750157600147880802976356884393432722622630310484818797322194401415620712660275179306324605679053723609 | CO_9911065276032760076739005 | 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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| null | null | null | null | 5.308622 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T14:59:31 | 4589481735574930793962480699698628302038261526889689516387627958646224543511649654771023215055909199302308954872907009453283124543542462015351784956928229 | PO_4589481735574930793962480 | null | null | null | [
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| 2023-12-01T23:04:57 | 9913871659973224673133611840225846464318662049970527307068176666025498256668766720679233452093499194085346022751237674431851758977198371874796884625922627 | CO_9913871659973224673133611 | 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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| 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 | 9930144399449065301262018168121847467580555607746330716185282370013419791152377065851080062125678150547102362787496055807063602542241303883401870233263089 | CO_9930144399449065301262018 | 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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| 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 | 9941227653572345651789415648128558182861355241045037221133220124058091373413301409542570388346257096688013245985789910599337998830493610430906715012523057 | CO_9941227653572345651789415 | 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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| null | null | null | null | 30.536528 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T15:16:54 | 10813252773219990335607450996202865300341378328450097631815288149857772219264836557169816575335260854502036909012379657044500233885755912833210200169410904 | PO_1081325277321999033560745 | null | null | null | [
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| 2023-12-01T23:04:57 | 9969037681863383973342413237849437044586753835214713354045822955127373165365670945855985133245896820247800737362200628758906476494857631582811486250162268 | CO_9969037681863383973342413 | 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 |
Al2As6GaO | Al2As6GaO | A6B2CD | [
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| 2023-12-01T23:04:57 | 996920143464927847007912582441168374780947048388373833654254437025558061649661368802695222719397561110525173947964160239673561428417391359073601440805017 | CO_9969201434649278470079125 | 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 |
Al6NOP2 | Al6NOP2 | A6B2CD | [
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7
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| [
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| [
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| 4 | 10 | [
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| 2023-12-01T23:04:56 | 9986919192910145958647525698294161642406339338927437179085376039553920941364659006201087282927826028357331829729793191181593397031603374593475142955257927 | CO_9986919192910145958647525 | 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 |
BaBe6InTl2 | BaBe6InTl2 | A6B2CD | [
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4,
4,
4,
4,
81,
81,
56,
49
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| [
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| 4 | 10 | [
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| null | null | null | null | 15.415306 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T14:46:18 | 1850867466978694016126517152772228130840506120113561633580327755400688193403490535460609391044222484118507358519956304389398751020640113343463064570377246 | PO_1850867466978694016126517 | null | null | null | [
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| 2023-12-01T23:04:57 | 998795019100057999476414902330234876483322984558768228898250735373665993148718483306911409075742318691886413210729509185977260734902440185181489020222295 | CO_9987950191000579994764149 | 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 |
C2N6PSb | C2N6PSb | A6B2CD | [
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15
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| [
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| 4 | 10 | [
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| 2023-12-01T23:04:56 | 9988819209781187621509119291826318291245806381150216170917234567751508158433822000852041906089568878694487297737963211550205548966947680273183635382815946 | CO_9988819209781187621509119 | 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 |
CKMg2Te6 | CKMg2Te6 | A6B2CD | [
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12
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| [
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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 |
Ar6ClNa2Rb | Ar6ClNa2Rb | A6B2CD | [
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| [
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"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 |
Ar2As6CaTl | Ar2As6CaTl | A6B2CD | [
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18,
18
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| [
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| 2023-12-01T23:04:57 | 9995838938524533033161624813541288614557870023085179675617986547256519487867385155762775506347040151364882368634934975538948230746792908882647534532561120 | CO_9995838938524533033161624 | 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 |
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