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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Ca2He6NaXe | Ca2He6NaXe | A6B2CD | [
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2,
2,
2,
2,
54
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| [
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| 2023-12-01T23:04:56 | 11758640997110614724193334640119459403338545037918025452057832687376473526789685931350217256275983420045516193607862429361920870950250560250011073139165962 | CO_1175864099711061472419333 | 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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| 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 |
H2ArMgO6 | ArH2MgO6 | A6B2CD | [
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18,
8,
8,
8,
8,
8,
8
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| [
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| 2023-12-01T23:04:57 | 11758990713002561234631292007986316148369170985553871875579387018127748676678303912995457698003998747826602988737137881532757427205025025601215911213418472 | CO_1175899071300256123463129 | 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 |
Br6Ge2MgPb | Br6Ge2MgPb | A6B2CD | [
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32,
32,
12,
35,
35,
35,
35,
35,
35
]
| [
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| [
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| 2023-12-01T23:04:57 | 11770380820146741636614956556182858546622648906347454765535149807211768459279719027599325242479554692979180868969075242429382358771334696068212278721474122 | CO_1177038082014674163661495 | 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 |
CAs2OS6 | As2COS6 | A6B2CD | [
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| [
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| null | null | null | null | 9.840858 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T14:13:00 | 1294474642647452040758221604066651679722983576195602369926718797038268290169197771566500787681247539190102369051533450607606430162395800447665008374078037 | PO_1294474642647452040758221 | null | null | null | [
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| 2023-12-01T23:04:56 | 11774642449083791001709865671300631807982720165967977452766408134094364251711868063002130206838551539489908145753680133698838660686975674273145972372433875 | CO_1177464244908379100170986 | 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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| 2023-12-01T23:04:57 | 11792386078259264819216405511075005805512575798825682221929341809527757404038335956033269746155315350675423602426764713912302898926910731707022712824659877 | CO_1179238607825926481921640 | 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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| 2023-12-01T23:04:57 | 11794382184615321869317963806205996794596563136133087597536829423092499544108198743650755338044133659239646806722301604809109683295253562005675904702209675 | CO_1179438218461532186931796 | 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 | 11795975938411661652440593690569473689690650141797999497750607252077134714429934756038940966591450472032526674672915163835812766627036325845969639233889587 | CO_1179597593841166165244059 | 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 |
GaS6Si2Sn | GaS6Si2Sn | A6B2CD | [
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| null | null | null | null | 5.513702 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T15:02:02 | 10657109727622799561647302605805168265659894510429658716165096680284174363792108288634736321589804802861799937633034535154035697818297007236285155361079119 | PO_1065710972762279956164730 | null | null | null | [
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| 2023-12-01T23:04:57 | 11801473639703307528119945307733421929252882948368336642270068461440839045938693712855536578728797815614680689657700545098851113247698521729142099385012833 | CO_1180147363970330752811994 | 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 |
AsGa6Si2Sn | AsGa6Si2Sn | A6B2CD | [
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| 2023-12-01T23:04:57 | 11801884571999630441497560279317248566307912931961089031780869909321154861216968969298825876362785328200669385683261510038008494460619647821327980325346833 | CO_1180188457199963044149756 | 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 | 11807068709219641611494091316629479259140933379627348487266042247375028010258560350712471529952060668678141740401110552318253608908973851898179866465608037 | CO_1180706870921964161149409 | 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 |
Ca2GaRbTe6 | Ca2GaRbTe6 | A6B2CD | [
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| 2023-12-01T23:04:57 | 11808174973394502257548227394358099788448548626127007160348303981586217305939209127193806504515494936668680314221135946638588069242357614482334389872045488 | CO_1180817497339450225754822 | 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 |
AlN6Sb2Sn | AlN6Sb2Sn | A6B2CD | [
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| [
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| null | null | null | null | 11.747928 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T14:48:15 | 6780764640362058170118679287416553733349733139249073360885278746422998480300734820379863551561890559650278236812327542744816172565030600364241784848825473 | PO_6780764640362058170118679 | null | null | null | [
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| 2023-12-01T23:04:56 | 11812253521179510610072785407239137990208952511994914811054405308454967245001300766612999209979174768407302784886470608449769744185569662163261920110833476 | CO_1181225352117951061007278 | 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 |
AlAs2GaSi6 | AlAs2GaSi6 | A6B2CD | [
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| null | null | null | null | 9.670587 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T14:45:36 | 5454766098780668395490401147323975269548642387083311806924017579090631065269532975182889976592650302032761842958304884439680352589329519117686449408148946 | PO_5454766098780668395490401 | null | null | null | [
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| 2023-12-01T23:04:56 | 11815072170693029158548071816528996872034308894491158272826565937193702950622397103853031118091180642182970228366291356130347290777049558335076341109263013 | CO_1181507217069302915854807 | 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 | 11819721594521340044554546029722212205937776476648207850199327155778552258975747012088594271907367452057675708178380626977998601030350476915362394298897975 | CO_1181972159452134004455454 | 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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| [
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| 2023-12-01T23:04:57 | 11829335952360894102667970291772258727505604690677509789189315474155309325075556030430814470041077126707826229988870608499069423694415391739178841434824106 | CO_1182933595236089410266797 | 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 | 11834238609514923683842543919973968626484405800711353789728324859921150164836449056883672459580169069011664525874082758716129736762874207289866301144954667 | CO_1183423860951492368384254 | 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 |
CAs2GaO6 | As2CGaO6 | 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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| 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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| 2023-12-01T23:04:56 | 11919564166410529961414891738431731310099696292146288649725338953569080037122015431184583734453534632038318031189229479056093635270766274519679294759076983 | CO_1191956416641052996141489 | 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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| 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 |
HMgTl6Xe2 | HMgTl6Xe2 | 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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| 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 | 1193401467539703157638009009453587460776251370470326697943682445712819715504780903242386694466652577421817631395996702001145974144190497598970361156571124 | CO_1193401467539703157638009 | ABC2D6-16_PRL_2018 | [
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"Rickard Armiento"
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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 | 8.464022 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T14:38:47 | 2761877406076815434962653420146272706337464635484121917443754056346979725506842847663624496870049642474645045143935606555437304737752166681381504214869867 | PO_2761877406076815434962653 | null | null | null | [
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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 |
GeOS2Sb6 | GeOS2Sb6 | A6B2CD | [
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| 2023-12-01T23:04:56 | 11966255233364333958569615444348367136771419952869194171397450856847894482900237772641206358569588566811375655030479529230114804313627601537539329704875835 | CO_1196625523336433395856961 | 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 |
CP2S6Sb | CP2S6Sb | A6B2CD | [
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| null | null | null | null | 13.193402 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T14:38:57 | 11049237448637227778170228822283248562577121085432308297308283771812101705261151452501858768282694749194804874291863290258942903777005223380735318181462133 | PO_1104923744863722777817022 | null | null | null | [
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| 2023-12-01T23:04:57 | 1196638311690291702643207249640148625183940275383677670416479994514059785969216801391916556491862039210538130666891671742086291625473440389711497021952930 | CO_1196638311690291702643207 | 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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| 2023-12-01T23:04:56 | 11979337146412119095068855857124286116695681850113871672034632372759107176651339620915851264772375204852422749651164242578098049820233905004673725558662011 | CO_1197933714641211909506885 | 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 |
Ba6IIn2Se | Ba6IIn2Se | A6B2CD | [
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| [
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| 2023-12-01T23:04:57 | 11982713311270628181338789548149901168798658663706895562184397046912348729376048607723186127502349776200551492958177334668865377049140925842986620742315273 | CO_1198271331127062818133878 | 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 |
GaNO2Sb6 | GaNO2Sb6 | A6B2CD | [
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| null | null | null | null | 9.877033 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T14:18:09 | 2972081952421452744371019655865258259052422763250697610270970685789680687738019523503986533513351885266371449347764603112292336025141915349409845364500094 | PO_2972081952421452744371019 | null | null | null | [
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| 2023-12-01T23:04:56 | 11983225114245928296322135766686957801638442062728479737004828929597056119250108113838997473728826948046353973326945174107123523058254380528939970510043905 | CO_1198322511424592829632213 | 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 |
Ge2OP6Sb | Ge2OP6Sb | 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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| 2023-12-01T23:04:57 | 11992672838046366732858427405562188864021813857307798631961474391632874058739469039191406607613866905382685179641969757332838008859809562216130442105599158 | CO_1199267283804636673285842 | 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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| 2023-12-01T23:04:57 | 11996423698805154593651053962567702841361858683477376543001633778655468026259200670311028577866260669796691907416080167605578925874434548768124029644578682 | CO_1199642369880515459365105 | 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 |
As2NOP6 | As2NOP6 | A6B2CD | [
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| null | null | null | null | 7.748034 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T14:33:20 | 13041444178311179211974941929793307543626442449881801445856645897191198497778504502217338707092731400177887165955873786130755418575477084242541904985196451 | PO_1304144417831117921197494 | null | null | null | [
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| 2023-12-01T23:04:57 | 11996967700612090758201410764968785956997930799174339438028492065090930327369327329347154269658926592128354940074388457962193381128202010220212844684180048 | CO_1199696770061209075820141 | 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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| 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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| 2023-12-01T23:04:56 | 12042795558552929141314363907076044936969841674527041088708502746860854753532557707618796444036516368147832226142805803327141710767655085067964743359748178 | CO_1204279555855292914131436 | 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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| 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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| 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 | 12064381835660580980165242989550883383183950243917952654474631778435863360597762932074779979225244717373337527095884490216942658324618616753984548783849299 | CO_1206438183566058098016524 | 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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| 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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AsGa6O2Sn | AsGa6O2Sn | A6B2CD | [
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| [
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| null | null | null | null | 7.307184 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T15:30:31 | 11004316097138667185169688203148543075861501743547793162415564413808085162132694926970573694930618529022306229887398802229368882422148048275429246626154642 | PO_1100431609713866718516968 | null | null | null | [
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| 2023-12-01T23:04:56 | 1207741861018665944235422225331095832902544422443722491065945577301783715195681423326033231911005663404682595081389167682764338490650025598531665036770505 | CO_1207741861018665944235422 | 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 |
Be2MgPSr6 | Be2MgPSr6 | A6B2CD | [
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15
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| [
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| 2023-12-01T23:04:57 | 12080818209047994912954906533637928281119653100985747944850239867753644497678964771178969520963441924634926668562296508186100696130685705526969548440428355 | CO_1208081820904799491295490 | 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 |
Ar2Cs6MgS | Ar2Cs6MgS | A6B2CD | [
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| [
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| 2023-12-01T23:04:56 | 12082751869660676213777828880509762794640814517953331831212711379175272200331281038666593254324065244058045710633891203994112218172523073455471829131273176 | CO_1208275186966067621377782 | 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 |
AsGe2P6Sn | AsGe2P6Sn | A6B2CD | [
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| [
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| null | null | null | null | 11.01744 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T14:14:23 | 9970876168611219113886445308033088022556142600722505186750691948593936424257347665983956635577013472834330065683461291371599234343945265823570128933503452 | PO_9970876168611219113886445 | null | null | null | [
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| 2023-12-01T23:04:56 | 12085349683092314818923940825924944373703779904580620715437740965810405219397095265412240176152719786517416334584953250525037362589284191799413403354411876 | CO_1208534968309231481892394 | 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 | 1208669227353516417647424101811772707716701711348446345342900800985660248995401254525708301785051436477838647248137782834721297861895605550935810213126404 | CO_1208669227353516417647424 | 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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]
| 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 | 12092589844221254776885641459058828618153720369900038862375461667963847497881032171221911507004244608064947788862198214507105539839613617375721552639560057 | CO_1209258984422125477688564 | 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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| 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 |
C2AlAs6P | AlAs6C2P | A6B2CD | [
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| [
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| 3 | 5619773740191231362411080110719461792163227237453978781448027420071717130477516227914170668123692139822126705814067712046125414884769380180651601053150470 | 1 | VASP 5.2.2 | DFT-PBE | null | []
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| null | null | null | null | 19.214894 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T14:35:16 | 10683506431424164496428251221426630061517629446122992952657254054577962401009354839948906780855961296963717832793542156130553841476644295326869461821386312 | PO_1068350643142416449642825 | null | null | null | [
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| 2023-12-01T23:04:57 | 12111614618796813070982977785910620699504144086754777555277873095062561008887010035548580066055776905237838257277707587816518368911571024582499500015196859 | CO_1211161461879681307098297 | 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 |
C6GaGe2Sn | C6GaGe2Sn | A6B2CD | [
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31
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| [
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| 2023-12-01T23:04:56 | 1212536793117802781082832974293205607113376755227053748543273687476697574867550937336897162322760318582360348895564667026143341417635441110535933509784933 | CO_1212536793117802781082832 | 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 |
AlGe6SbSn2 | AlGe6SbSn2 | A6B2CD | [
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13
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| [
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| 4 | 10 | [
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| 3 | 2177720475618202530130613602359316215090372199060199628010945901305869795539741198677452867038453326592380330668408383683791122151197054312370820503565592 | 1 | VASP 5.2.2 | DFT-PBE | null | []
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| null | null | null | null | 6.464102 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T14:50:35 | 12865094617432258188008139138418715817426675005920081580966063424120765081594745523436026851296001841880114863015494027870604329370833827332372940187281567 | PO_1286509461743225818800813 | null | null | null | [
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| 2023-12-01T23:04:56 | 12126151923219981960798530775936545641519021580092418626311701639090136020987600965908226640184687122001266916461109484537225822999002006242332678253479321 | CO_1212615192321998196079853 | ABC2D6-16_PRL_2018 | [
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"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 |
Ge6KrLiSn2 | Ge6KrLiSn2 | A6B2CD | [
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32,
32,
32,
32,
32,
32,
50,
50,
3
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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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| 2023-12-01T23:04:56 | 12187234744723155277620172017202307551708876894165579547034843170870569474356302522104519493329249879844536058520984637803867311287506036425574276878708864 | CO_1218723474472315527762017 | 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 | 12191488778987573139419753266787777899917957996098906234387180266173509601842810201416481462274879166347120042245427442042201704497463756442123044819830684 | CO_1219148877898757313941975 | 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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"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 |
BBeGa6Xe2 | BBeGa6Xe2 | A6B2CD | [
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5,
31,
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31,
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54
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| [
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| null | null | null | null | 7.827368 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T14:17:40 | 7936615219865218911906082561322602572161558186217902131313579753928823502470355151253516686299176982258386046675925656640602940504570049402374606849674606 | PO_7936615219865218911906082 | null | null | null | [
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| 2023-12-01T23:04:57 | 12217661960499086546469956336688464788349447009244841921680167529369949825498876117346089127218130344667670950541517563195759233974525138108077238148387227 | CO_1221766196049908654646995 | 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",
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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 | [
[
1,
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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 |
AlO2SbSi6 | AlO2SbSi6 | A6B2CD | [
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8
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| [
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| null | null | null | null | 14.588707 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T14:49:02 | 3415805602799304488328944347329531344760354372557321573721676080420455350691729760673045932067714862856545970170757843668024773060528507269816291848414347 | PO_3415805602799304488328944 | null | null | null | [
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]
| [
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| 2023-12-01T23:04:56 | 12219721418614519275020701126223638036687567022862524815607571855515441052190153091483142907591133716731916585407950673917127242138938089737792888493867504 | CO_1221972141861451927502070 | 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 | [
[
1,
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| [
3
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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 |
KKrMg6Pb2 | KKrMg6Pb2 | A6B2CD | [
82,
82,
36,
19,
12,
12,
12,
12,
12,
12
]
| [
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| [
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| 3 | 9236412557127676763298605695860347681352496581712757140094742024012026336733947887038320661413899094763077529612419507581916734831229549376320238450332933 | 1 | VASP 5.2.2 | DFT-PBE | null | []
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| null | null | null | null | 10.63117 | null | null | {"input": {"encut": {"value": 600, "unit": "eV"}, "kpoints": "3 x 3 x 3"}, "hash": "3729522099721817188397416740516810902027432225271784584363856857132107150877135363641369004947328165181781344107197484170910728217675499016271602255113675", "id": "MD_3729522099721817188397416"} | MD_3729522099721817188397416 | 2024-08-16T14:19:35 | 11765431482876180527645260090522399608352555494683668580793932283421117592781711795327920092964024283787158783501412769157561750911887804102524194184305875 | PO_1176543148287618052764526 | null | null | null | [
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| [
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| 2023-12-01T23:04:57 | 122308550840279609933966786850221587299825133683406449937554613598761106476667308585655263106729991143531905810443496165206858506169109609633415561377735 | CO_1223085508402796099339667 | 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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| [
3
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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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