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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Al3Ti4 | Al3Ti4 | A4B3 | [
13,
13,
13,
22,
22,
22,
22
]
| [
"Al",
"Ti"
]
| [
0.42857142857142855,
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| 2 | 7 | [
[
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0
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[
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0
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[
0,
0,
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[
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[
0,
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[
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| [
true,
true,
true
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| [
1,
1,
1
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[
0.000004,
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[
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],
[
-6.241509125883259e-8,
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]
]
| true | null | null | -45.0094 | null | 0.035633 | 0.12462 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | 2024-08-16T15:04:32 | 12580494806838126434489092269946539806718232436966042814525965884936955095983346167539368729344077649907620945300014905835696619364881222391652115289107866 | PO_1258049480683812643448909 | null | null | null | [
"train_2nd_stage_588"
]
| [
"DS_dtjyh96dypuu_0"
]
| 2023-12-01T23:19:00 | 12260898877335668845047437698807871433563699158587397616404297088413565776100705553951400782510105194896785808317546770771164109307809740198791732757785021 | CO_1226089887733566884504743 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
"Evgeny V. Podryabinkin",
"Gus L.W. Hart",
"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
"Ni",
"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.2966449914229864,
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]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1016/j.commatsci.2018.09.031', 'source-data': 'https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data', 'other': None} | 10.60732/7b56ca82 | 3389360456198058392034570193391541356928879716971205247655861353037594036502163554880877085823897366490724947053850262613353186244287689034639073555582445 | DS_dtjyh96dypuu_0 | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0 |
Ni2Ti4 | NiTi2 | A2B | [
28,
28,
22,
22,
22,
22
]
| [
"Ni",
"Ti"
]
| [
0.3333333333333333,
0.6666666666666666
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| 2 | 6 | [
[
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[
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[
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[
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[
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[
0.52782,
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]
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| [
true,
true,
true
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| [
1,
1,
1
]
| 3 | 6905835957727867353007433387458975835455455987883915033068306608722739363535557273222479173475179500914523040034305766644776281512647124206213134401126056 | 1 | VASP | DFT | null | [
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| null | [
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]
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| true | null | null | -42.921587 | null | 0.000166 | 0.000262 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | 2024-08-16T15:29:15 | 8721031045562797221682131711394286278869985906685513578579437897453577920423737174172871000522630482076665073676865945841752520978151658278832861271172097 | PO_8721031045562797221682131 | null | null | null | [
"train_2nd_stage_874"
]
| [
"DS_dtjyh96dypuu_0"
]
| 2023-12-01T23:19:00 | 1342983214450349413181238368954880967454810169504965233402653068849093392247629992463053051974012203868676982425385497487293273725347497943230669475788275 | CO_1342983214450349413181238 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
"Evgeny V. Podryabinkin",
"Gus L.W. Hart",
"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
"Ni",
"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.2966449914229864,
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]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1016/j.commatsci.2018.09.031', 'source-data': 'https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data', 'other': None} | 10.60732/7b56ca82 | 3389360456198058392034570193391541356928879716971205247655861353037594036502163554880877085823897366490724947053850262613353186244287689034639073555582445 | DS_dtjyh96dypuu_0 | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0 |
Ni4Ti8 | NiTi2 | A2B | [
28,
28,
28,
28,
22,
22,
22,
22,
22,
22,
22,
22
]
| [
"Ni",
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| [
0.3333333333333333,
0.6666666666666666
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| 2 | 12 | [
[
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[
-4.66388,
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0.068255
],
[
-1.68108,
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| [
[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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2.29384
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[
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2.3546
]
]
| [
true,
true,
true
]
| [
1,
1,
1
]
| 3 | 5708731760930537344353712041013302599482701983333600364214195563992748252258153586019421696053652301915386097732661139794208308490422261229837527612998058 | 1 | VASP | DFT | null | [
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[
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[
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[
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[
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[
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[
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[
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| null | [
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],
[
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]
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| true | null | null | -87.684899 | null | 0.058251 | 0.078674 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | 2024-08-16T15:24:18 | 4600740155910729107794450237890146001028799287924433381960594699611060501178360509638011073489615602626981119410519016444476201154754656383403750146277602 | PO_4600740155910729107794450 | null | null | null | [
"train_1st_stage_764"
]
| [
"DS_dtjyh96dypuu_0"
]
| 2023-12-01T23:19:00 | 4417010473679714889384596085644006816628601576628379599237224443342471028316787449585612157975817967408793565091127063221782182091211636010886454972872695 | CO_4417010473679714889384596 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
"Evgeny V. Podryabinkin",
"Gus L.W. Hart",
"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
"Ni",
"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.2966449914229864,
0.3824550205449396,
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]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1016/j.commatsci.2018.09.031', 'source-data': 'https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data', 'other': None} | 10.60732/7b56ca82 | 3389360456198058392034570193391541356928879716971205247655861353037594036502163554880877085823897366490724947053850262613353186244287689034639073555582445 | DS_dtjyh96dypuu_0 | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0 |
Ni2Ti4 | NiTi2 | A2B | [
28,
28,
22,
22,
22,
22
]
| [
"Ni",
"Ti"
]
| [
0.3333333333333333,
0.6666666666666666
]
| 2 | 6 | [
[
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1.39636,
2.13412
],
[
4.29076,
4.28615,
-1.39274
],
[
-1.46749,
1.61571,
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]
]
| [
[
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[
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[
0.73965,
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[
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],
[
-0.33631,
3.03911,
-3.28894
],
[
1.77871,
4.56313,
-4.77651
]
]
| [
true,
true,
true
]
| [
1,
1,
1
]
| 3 | 5294782588272464583740031571217102897242378207714758643236042623819470961304866470724946919094877743588243640356951572636891738318907141416794388822472477 | 1 | VASP | DFT | null | [
[
0.052737,
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0.185545
],
[
-0.052181,
0.213404,
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],
[
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[
0.156254,
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[
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0.105038,
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],
[
0.116485,
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0.110079
]
]
| null | [
[
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],
[
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],
[
0.007121811572997833,
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]
]
| true | null | null | -43.586091 | null | 0.242303 | 0.287546 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | 2024-08-16T14:48:58 | 550956709857981627358311915653914182978003575083737476769133775034637737622724472094926503762799712361892863792984966491795614532882032942343405547531441 | PO_5509567098579816273583119 | null | null | null | [
"train_2nd_stage_692"
]
| [
"DS_dtjyh96dypuu_0"
]
| 2023-12-01T23:19:00 | 797232131965981619950846959761649894943394020792010714125897660401754167568481484090665234779280853560450386494451503205178662996511889087164196560975799 | CO_7972321319659816199508469 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
"Evgeny V. Podryabinkin",
"Gus L.W. Hart",
"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
"Ni",
"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.2966449914229864,
0.3824550205449396,
0.32089998803207403
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1016/j.commatsci.2018.09.031', 'source-data': 'https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data', 'other': None} | 10.60732/7b56ca82 | 3389360456198058392034570193391541356928879716971205247655861353037594036502163554880877085823897366490724947053850262613353186244287689034639073555582445 | DS_dtjyh96dypuu_0 | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0 |
Al13Ti | Al13Ti | A13B | [
13,
13,
13,
13,
13,
13,
13,
13,
13,
13,
13,
13,
13,
22
]
| [
"Al",
"Ti"
]
| [
0.9285714285714286,
0.07142857142857142
]
| 2 | 14 | [
[
2.87099,
-0.003438,
-0.008321
],
[
-1.41906,
2.49891,
4.6305
],
[
-0.004186,
-14.8016,
4.67609
]
]
| [
[
1.44775,
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9.28498
],
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 2023-12-01T23:19:00 | 11049082977551800470747774093882178817548784747472837487658155696033800086880313345616271318250970984499719097728673189006614238997654683476379022191805453 | CO_1104908297755180047074777 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| 2023-12-01T23:19:00 | 10613503131802148046270747972180835379493144367717913024093698165161931998173348982278630538796885778137006918538256448385870630849168683046375769340162971 | CO_1061350313180214804627074 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
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"Ti"
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| [
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| 2023-12-01T23:19:00 | 7484795582547888017433404306867475719676796531758846820155987735098050315027406597737922660928233493421571340618450174541568693829743710388974074789400088 | CO_7484795582547888017433404 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
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"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
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"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
[
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Al2Ni | Al2Ni | A2B | [
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28
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| [
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| 2023-12-01T23:19:00 | 620309917516280160247937164851080694400283219150198684089273154764637216398537128134543626707688703485967541296463841151374307147386401850558660746025713 | CO_6203099175162801602479371 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
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"Gus L.W. Hart",
"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
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"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
[
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1016/j.commatsci.2018.09.031', 'source-data': 'https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data', 'other': None} | 10.60732/7b56ca82 | 3389360456198058392034570193391541356928879716971205247655861353037594036502163554880877085823897366490724947053850262613353186244287689034639073555582445 | DS_dtjyh96dypuu_0 | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0 |
AlNi4Ti3 | AlNi4Ti3 | A4B3C | [
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| [
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| [
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| 2023-12-01T23:19:00 | 7989850277653807355759377712928501800810368848984448402503266605768796660972471066126232726036969217081662139797514830572542026681071680146468972862488887 | CO_7989850277653807355759377 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
"Evgeny V. Podryabinkin",
"Gus L.W. Hart",
"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
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"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
[
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1016/j.commatsci.2018.09.031', 'source-data': 'https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data', 'other': None} | 10.60732/7b56ca82 | 3389360456198058392034570193391541356928879716971205247655861353037594036502163554880877085823897366490724947053850262613353186244287689034639073555582445 | DS_dtjyh96dypuu_0 | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0 |
Al2Ni7Ti | Al2Ni7Ti | A7B2C | [
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22
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| [
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| 3 | 10 | [
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| true | null | null | -58.597261 | null | 0.010272 | 0.015551 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | 2024-08-16T14:41:57 | 4155135663994030647498348174819288076997645193942497383632970980654862788513621781182293967825581969893693053220065843232822142585141943565957163163845641 | PO_4155135663994030647498348 | null | null | null | [
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| [
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| 2023-12-01T23:19:00 | 11284111194411719767668460710895249667757632592364528521892341662469915513519325275193889426091912217424747428077795670560662001260739229901901685412187869 | CO_1128411119441171976766846 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
"Evgeny V. Podryabinkin",
"Gus L.W. Hart",
"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
"Ni",
"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
[
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| [
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| 2023 | [
0.2966449914229864,
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1016/j.commatsci.2018.09.031', 'source-data': 'https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data', 'other': None} | 10.60732/7b56ca82 | 3389360456198058392034570193391541356928879716971205247655861353037594036502163554880877085823897366490724947053850262613353186244287689034639073555582445 | DS_dtjyh96dypuu_0 | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0 |
Ni2Ti2 | NiTi | AB | [
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22,
22
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| [
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| [
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| 2 | 4 | [
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[
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[
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| 2023-12-01T23:19:00 | 10981296050883187545496894609673637110423631789072280338213147572004131548487179104421113713550529287786005197294521697432078131279545233671315633580683467 | CO_1098129605088318754549689 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| 2023-12-01T23:19:00 | 4946906679414006227700639817756158111902885159121222879197919957163717060524998535480403894593955561861189559166011846623791417925301054229206009807977046 | CO_4946906679414006227700639 | AlNiTi_CMS_2019 | [
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]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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"Ti"
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 2023-12-01T23:19:00 | 3704360277430767456246552612388209758189623494860484673415844330774789159350573282828368695555215842864642836221039269384433550834577067118840832577817757 | CO_3704360277430767456246552 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 2023-12-01T23:19:00 | 3293694791320327076131826534931361601374102098233570258985146708293904494149202203090045390684066010689547784399562815708399650544789054011634680982285031 | CO_3293694791320327076131826 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| 2023-12-01T23:19:00 | 5109264867017125509263512445440423132074993598327684749148829971468078852694357929009453213481018395272520511282208239659812120171996357884688448448139649 | CO_5109264867017125509263512 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| 2023-12-01T23:19:00 | 11438795462034243238261809813068771673026409378989912369663163792461780902620327053031487153328321759761591289834592109732506765604878049707567226412394382 | CO_1143879546203424323826180 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
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"Ti"
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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Al6Ni4 | Al3Ni2 | A3B2 | [
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| [
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| 2023-12-01T23:19:00 | 4586932451254523864341659367125918927125972201540135150811897964397016698060315800973488020436560898457409034257121370692398071874701722324303815108983020 | CO_4586932451254523864341659 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
"Evgeny V. Podryabinkin",
"Gus L.W. Hart",
"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
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"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
[
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1016/j.commatsci.2018.09.031', 'source-data': 'https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data', 'other': None} | 10.60732/7b56ca82 | 3389360456198058392034570193391541356928879716971205247655861353037594036502163554880877085823897366490724947053850262613353186244287689034639073555582445 | DS_dtjyh96dypuu_0 | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0 |
AlNi2Ti2 | AlNi2Ti2 | A2B2C | [
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| 2023-12-01T23:19:00 | 4382562126319898886963725690369270280868642111091949944710691629852779207819996481905698015528111540908455955584536911871516732442419283657767844038165315 | CO_4382562126319898886963725 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
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"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
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"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
[
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| 3 | 7122101238723051596021906468946583029382202964352985272374339712819784177663412032357009374052787166477646195741699042334250170597457209862496637190496142 | 1 | VASP | DFT | null | [
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| [
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| 2023-12-01T23:19:00 | 1394166250097841913962091414356119102097558239296577719010060486963371822291064908228084193600034971178282492898367130346216970030167949515176802768050653 | CO_1394166250097841913962091 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
"Evgeny V. Podryabinkin",
"Gus L.W. Hart",
"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
"Ni",
"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
[
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1016/j.commatsci.2018.09.031', 'source-data': 'https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data', 'other': None} | 10.60732/7b56ca82 | 3389360456198058392034570193391541356928879716971205247655861353037594036502163554880877085823897366490724947053850262613353186244287689034639073555582445 | DS_dtjyh96dypuu_0 | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0 |
Al2Ni2 | AlNi | AB | [
13,
13,
28,
28
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| [
"Al",
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| [
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| 2 | 4 | [
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| 3 | 5298273088571239885647478809335191112689316450518712167566201017897306646155274062549878357869607979497895344945773339182202207011298792550856017608411348 | 1 | VASP | DFT | null | [
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| true | null | null | -19.631152 | null | 0.000003 | 0.000005 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | 2024-08-16T14:29:50 | 11523512341345409781301642993236424121228356715556604736916275680035759704298512898813333029596800109444261135244382920017828388343059997565923004115207821 | PO_1152351234134540978130164 | null | null | null | [
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| 2023-12-01T23:19:00 | 9734556497270825196957054956679418917843356763826954460314935336782202659579146197595919631405457668191913329446074662822276790944053158486024466005609568 | CO_9734556497270825196957054 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
"Evgeny V. Podryabinkin",
"Gus L.W. Hart",
"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
"Ni",
"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
[
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1016/j.commatsci.2018.09.031', 'source-data': 'https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data', 'other': None} | 10.60732/7b56ca82 | 3389360456198058392034570193391541356928879716971205247655861353037594036502163554880877085823897366490724947053850262613353186244287689034639073555582445 | DS_dtjyh96dypuu_0 | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0 |
AlNi9Ti2 | AlNi9Ti2 | A9B2C | [
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| [
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| 3 | 12 | [
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| 2023-12-01T23:19:00 | 4157848403109767749487341077314184494335841383946723807055209993256706175707668257150653404846279955766628225141979530720090951616090915867245019324133710 | CO_4157848403109767749487341 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 2023-12-01T23:19:00 | 6232817291607293813580968090629327478643466217140971718429680183197398812074880346188611043504530048247012067611996177885043894198056357558531070205773735 | CO_6232817291607293813580968 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| 2023-12-01T23:19:00 | 4921489707323719940708589492799312799806474627538457021271216855544608721903366191037313750578886867312610185141876579989689107079011150451059759819441617 | CO_4921489707323719940708589 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
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"Ti"
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| 2023-12-01T23:19:00 | 10901347823483070867085865180250458749883475506243687462043546004168365874461613992078577847174403111193347642032119798035567036741958949111693968575214292 | CO_1090134782348307086708586 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 2023-12-01T23:19:00 | 2880810843849789496962947553143729480214280011396545061350729751982563994287263000049326777697437642794537029020694927004046538147105503981070427640219635 | CO_2880810843849789496962947 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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Ni2 | Ni | A | [
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| true | null | null | -7.25625 | null | 0.000185 | 0.000185 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | 2024-08-16T14:21:41 | 9561958555317009314277281191257275404771645366602822320686061919291082759780021976860083561955373375745120894760358565182631972980179981834217903730394103 | PO_9561958555317009314277281 | null | null | null | [
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| 2023-12-01T23:19:00 | 3928835407951685882164096260681195206996057176733290451481573337733304838617591740917053365090991223140675632803972198246331763153798322829376275223615068 | CO_3928835407951685882164096 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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"Ti"
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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Al4Ti2 | Al2Ti | A2B | [
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| 2023-12-01T23:19:00 | 6655734374792780354871104645643664678677273282951857330704310324781577665858151539690271698400664335824390975272234822059492623532833713305142094524256572 | CO_6655734374792780354871104 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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"Ti"
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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Al5NiTi2 | Al5NiTi2 | A5B2C | [
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| 2023-12-01T23:19:00 | 12882447172394086196574763942043838108093088662285388891257669144842307556089955852662153705298364487493048327805971503366161097818899090801916930009996576 | CO_1288244717239408619657476 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
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]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1016/j.commatsci.2018.09.031', 'source-data': 'https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data', 'other': None} | 10.60732/7b56ca82 | 3389360456198058392034570193391541356928879716971205247655861353037594036502163554880877085823897366490724947053850262613353186244287689034639073555582445 | DS_dtjyh96dypuu_0 | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0 |
Al2Ti4 | AlTi2 | A2B | [
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| 2023-12-01T23:19:00 | 5815629773372453709990128990244528738309160418770004531522287964550441468144481408807683854321778385116621742361009862674796746865769327765486782864125723 | CO_5815629773372453709990128 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
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"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1016/j.commatsci.2018.09.031', 'source-data': 'https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data', 'other': None} | 10.60732/7b56ca82 | 3389360456198058392034570193391541356928879716971205247655861353037594036502163554880877085823897366490724947053850262613353186244287689034639073555582445 | DS_dtjyh96dypuu_0 | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0 |
Al6Ni4 | Al3Ni2 | A3B2 | [
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| [
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| 3 | 11062906916682034808665281722334242605959473749327074825714708647944616196374245574232093884358314297471581638032392065591442068912946204703534652505104417 | 1 | VASP | DFT | null | [
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| true | null | null | -49.898677 | null | 0.035379 | 0.069464 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | 2024-08-16T15:04:27 | 13128393556028913067175417279647284788865972143205930582856863327646628400842417778408230811341351411039355588971227706898304693192797384731869019593641490 | PO_1312839355602891306717541 | null | null | null | [
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| [
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| 2023-12-01T23:19:00 | 5121181691214028979730132200572443646955380610030659272691307671259915299218916594580307855615879448982477219842024997132538147224269092361524018619188126 | CO_5121181691214028979730132 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
"Evgeny V. Podryabinkin",
"Gus L.W. Hart",
"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
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"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
[
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| [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1016/j.commatsci.2018.09.031', 'source-data': 'https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data', 'other': None} | 10.60732/7b56ca82 | 3389360456198058392034570193391541356928879716971205247655861353037594036502163554880877085823897366490724947053850262613353186244287689034639073555582445 | DS_dtjyh96dypuu_0 | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0 |
Ni7Ti3 | Ni7Ti3 | A7B3 | [
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28,
28,
22,
22,
22
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| [
"Ni",
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| [
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| 2 | 10 | [
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[
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 2023-12-01T23:19:00 | 3815072139073858054246700706193068850234582522697985522346651958915772524910285903475826229726350486388989959154558580687079323124519130192249223746860080 | CO_3815072139073858054246700 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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Ni2Ti | Ni2Ti | A2B | [
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| 2023-12-01T23:19:00 | 9896381014285071973477529747564426055920622883439994422644489913535270202297902208390075977992754577750043516213776495052082885851112116740580708488345083 | CO_9896381014285071973477529 | AlNiTi_CMS_2019 | [
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"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
"Ni",
"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
[
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3
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1016/j.commatsci.2018.09.031', 'source-data': 'https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data', 'other': None} | 10.60732/7b56ca82 | 3389360456198058392034570193391541356928879716971205247655861353037594036502163554880877085823897366490724947053850262613353186244287689034639073555582445 | DS_dtjyh96dypuu_0 | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0 |
AlTi | AlTi | AB | [
13,
22
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| [
"Al",
"Ti"
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| [
0.5,
0.5
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| 2 | 2 | [
[
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[
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[
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| true | null | null | -12.384782 | null | 0.000001 | 0.000001 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | 2024-08-16T15:12:53 | 2381437129237046176507250627351019625108752010184620962610340077110479138625658957690486111125114979034805208938160531777834301778690916193233674758445880 | PO_2381437129237046176507250 | null | null | null | [
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| [
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| 2023-12-01T23:19:00 | 5543941203128159119218030360592184399865338582544645236816698709200682652034889243401284954162631861613517820128861765374989692145340044384893274899209719 | CO_5543941203128159119218030 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
"Evgeny V. Podryabinkin",
"Gus L.W. Hart",
"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
"Ni",
"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
[
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| [
3
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1016/j.commatsci.2018.09.031', 'source-data': 'https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data', 'other': None} | 10.60732/7b56ca82 | 3389360456198058392034570193391541356928879716971205247655861353037594036502163554880877085823897366490724947053850262613353186244287689034639073555582445 | DS_dtjyh96dypuu_0 | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0 |
Al2Ti10 | AlTi5 | A5B | [
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| [
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| [
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| 2023-12-01T23:19:00 | 3321284766802917768616575888232765173824514407159081729898363121291989503542617828135738289581540824273149739697895903326425138117896742073886746627701079 | CO_3321284766802917768616575 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
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"Gus L.W. Hart",
"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
"Ni",
"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
[
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3
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Al3Ni3 | AlNi | AB | [
13,
13,
13,
28,
28,
28
]
| [
"Al",
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| 2 | 6 | [
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[
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| 2023-12-01T23:19:00 | 6055750857056914267636880287263036732334391306546637183143405223176621563249524629697957772747038231286723853873909738767892614451417712158704332911086082 | CO_6055750857056914267636880 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
"Evgeny V. Podryabinkin",
"Gus L.W. Hart",
"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
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"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
[
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1016/j.commatsci.2018.09.031', 'source-data': 'https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data', 'other': None} | 10.60732/7b56ca82 | 3389360456198058392034570193391541356928879716971205247655861353037594036502163554880877085823897366490724947053850262613353186244287689034639073555582445 | DS_dtjyh96dypuu_0 | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0 |
Ni4Ti2 | Ni2Ti | A2B | [
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22,
22
]
| [
"Ni",
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| [
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| 2 | 6 | [
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| 3 | 13107711528461064784045803787897008179396480203892532223058924807176252820255284335845424139339868054332242559799090785493095892853195225341898205927764705 | 1 | VASP | DFT | null | [
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| true | null | null | -39.602805 | null | 0.422979 | 0.723795 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | 2024-08-16T15:29:32 | 3560487710143474791179837930310902151187798693822505069504193291543105429843953786890370112316599811797008252085928299999395629339214305640692973084149748 | PO_3560487710143474791179837 | null | null | null | [
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| 2023-12-01T23:19:00 | 2189004555477332367039842014299988341308767492506899818416547986654426122152306801967054006237590409765008017765808403773905784835460828017261657346665818 | CO_2189004555477332367039842 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
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"Gus L.W. Hart",
"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
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"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1016/j.commatsci.2018.09.031', 'source-data': 'https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data', 'other': None} | 10.60732/7b56ca82 | 3389360456198058392034570193391541356928879716971205247655861353037594036502163554880877085823897366490724947053850262613353186244287689034639073555582445 | DS_dtjyh96dypuu_0 | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0 |
Al5Ni6Ti | Al5Ni6Ti | A6B5C | [
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28,
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22
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| [
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| 3 | 12 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 2023-12-01T23:19:00 | 6140648144352383861439689834891063707250947478258016830646171812960726426998374962859902208362168865730077945533742235430743646118161057793821142805484850 | CO_6140648144352383861439689 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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"Ti"
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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AlNi2 | AlNi2 | A2B | [
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| 2023-12-01T23:19:00 | 12776580738341979640092224263724296429334641324690269377681749851960381693923891925268831133172445247238818964002009121062212311343731640318420587097895619 | CO_1277658073834197964009222 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| 2023-12-01T23:19:00 | 2412341932631184119745213039365670372549698929027108884447010676903718578367602851225377259795500537268457477032504326774836387013567271926143856681383036 | CO_2412341932631184119745213 | AlNiTi_CMS_2019 | [
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]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| true | null | null | -50.958912 | null | 0.000101 | 0.000133 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | 2024-08-16T14:23:00 | 13060466014806870436049842731156022519449188108272019442168587349484349213819726194135392982418128453518887108163387366797640537805191254909522561682029526 | PO_1306046601480687043604984 | null | null | null | [
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| 2023-12-01T23:19:00 | 6265028997515140263451765276692254210908676672742618885673882712670890350722344495082999039522083475826889157694882819481901374929320306789821204739483109 | CO_6265028997515140263451765 | AlNiTi_CMS_2019 | [
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]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
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"Ti"
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1016/j.commatsci.2018.09.031', 'source-data': 'https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data', 'other': None} | 10.60732/7b56ca82 | 3389360456198058392034570193391541356928879716971205247655861353037594036502163554880877085823897366490724947053850262613353186244287689034639073555582445 | DS_dtjyh96dypuu_0 | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0 |
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| [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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Al2Ni3Ti | Al2Ni3Ti | A3B2C | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| [
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| 2023-12-01T23:19:00 | 5960007138990170414300389747981120211294250422000644475092652389028178305794924897509484959547026403161883660394632954490066682324921016714789974876597831 | CO_5960007138990170414300389 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
"Evgeny V. Podryabinkin",
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"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
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"Ti"
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| [
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| 2023-12-01T23:19:00 | 5273842125522437139666410835514878339524702137714316662304630664728169567892863324572438320739765289006213799837238338376522135098669012840411539475443487 | CO_5273842125522437139666410 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
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]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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Al6Ni4 | Al3Ni2 | A3B2 | [
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| 2023-12-01T23:19:00 | 3859660003717193755086179361541853383192889278669060640464116488472855501110953273534149933674578203203802650382172941143854368989425857950492577637066657 | CO_3859660003717193755086179 | AlNiTi_CMS_2019 | [
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]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1016/j.commatsci.2018.09.031', 'source-data': 'https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data', 'other': None} | 10.60732/7b56ca82 | 3389360456198058392034570193391541356928879716971205247655861353037594036502163554880877085823897366490724947053850262613353186244287689034639073555582445 | DS_dtjyh96dypuu_0 | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0 |
Al10Ti6 | Al5Ti3 | A5B3 | [
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| 2023-12-01T23:19:00 | 8953723733495368591393536372411104995801283331056844314484536945742279253938531801056245189610116912254547779853748266731979091197456932868154343732541531 | CO_8953723733495368591393536 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
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]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
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"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1016/j.commatsci.2018.09.031', 'source-data': 'https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data', 'other': None} | 10.60732/7b56ca82 | 3389360456198058392034570193391541356928879716971205247655861353037594036502163554880877085823897366490724947053850262613353186244287689034639073555582445 | DS_dtjyh96dypuu_0 | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0 |
Al4Ni3Ti3 | Al4Ni3Ti3 | A4B3C3 | [
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| [
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| 2023-12-01T23:19:00 | 1322249865239946039385447802201526901532164423662034147912247715951924249261693569130190468862510839148123402467383417302269204321103022024639248147332116 | CO_1322249865239946039385447 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| [
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| 2023-12-01T23:19:00 | 710017986275929351981124503212158553445274499675115613646058852705485782238621730707978123004564432470026251803349495029850215906301115582604291150553173 | CO_7100179862759293519811245 | AlNiTi_CMS_2019 | [
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"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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Al3Ni7Ti2 | Al3Ni7Ti2 | A7B3C2 | [
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| [
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| true | null | null | -71.527432 | null | 0.194358 | 0.496071 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | 2024-08-16T14:45:01 | 13056390070247491964592654720927783431528631015599591902082341922370932693649929878149087746038262826341750502413856218544099815040921111196713691013494819 | PO_1305639007024749196459265 | null | null | null | [
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| 2023-12-01T23:19:00 | 12440379156989034943045132551208543698830195650580755122212796049193242499836088848728417613043193000555381113766611898158365686896311629949281720976907341 | CO_1244037915698903494304513 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
"Evgeny V. Podryabinkin",
"Gus L.W. Hart",
"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
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"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
[
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Al4Ni10 | Al2Ni5 | A5B2 | [
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| [
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| true | null | null | -76.074832 | null | 0.011052 | 0.024574 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | 2024-08-16T14:12:11 | 3811329462129981623741262891551686609089451437668728756763751215337559521593851488682986400997905973050062852501356987659623592969913884246852080229291289 | PO_3811329462129981623741262 | null | null | null | [
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| [
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| 2023-12-01T23:19:00 | 1293905396865499370827531574169145831718809899908199288565524491702976120166146952072103646536926914500750937747357287415061043223803757868059219450475738 | CO_1293905396865499370827531 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
"Evgeny V. Podryabinkin",
"Gus L.W. Hart",
"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
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"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
[
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1016/j.commatsci.2018.09.031', 'source-data': 'https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data', 'other': None} | 10.60732/7b56ca82 | 3389360456198058392034570193391541356928879716971205247655861353037594036502163554880877085823897366490724947053850262613353186244287689034639073555582445 | DS_dtjyh96dypuu_0 | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0 |
AlNi2Ti3 | AlNi2Ti3 | A3B2C | [
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22
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| [
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| [
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| 2023-12-01T23:19:00 | 11908725455937562769267932699090855195100279629011152465732927923125195035939475532429517335987445546169537162262929321582865807713092515511406499633075206 | CO_1190872545593756276926793 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| 2023-12-01T23:19:00 | 8108829520558488992400584517715115936036583716895046801162658786332505881404035955868378696713198641112262660537152281491289476260293090676345282806080869 | CO_8108829520558488992400584 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| 2023-12-01T23:19:00 | 7159751126619977051694814189180659494930693293002968505514031914955020980479260114662700035817655220302790675799682143891017028072196457135731774516673090 | CO_7159751126619977051694814 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| 2023-12-01T23:19:00 | 703465086602729929659016698107664668318571681346426850221293417780369248570571520288934847877711424350760516180106936518374154818651521196839638654749302 | CO_7034650866027299296590166 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
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]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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"Ti"
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| 2023-12-01T23:19:00 | 11103416305229055712739557218773375106509223388397137143829844184061793059740572067782923227539275544610819339350974547395971487108042065852055480715809253 | CO_1110341630522905571273955 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
"Evgeny V. Podryabinkin",
"Gus L.W. Hart",
"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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"Ni",
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| 2023-12-01T23:19:00 | 11950900669628507222587514332395152669952903361168928979049982771280696812540223971673493381071280910102606941641919151011336011817019900826282006079550951 | CO_1195090066962850722258751 | AlNiTi_CMS_2019 | [
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| 2023-12-01T23:19:00 | 7940414422205582271728006389686784874495513689290716525955286545759563330796585487951708744183639655899810185246053748796750475303366029746932995049774328 | CO_7940414422205582271728006 | AlNiTi_CMS_2019 | [
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| [
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| 2023-12-01T23:19:00 | 10831737274774922274168670077051830590830913638901981182993961093891298730762413546161836720963397088429983197535279371198060842356885080239070955443628211 | CO_1083173727477492227416867 | AlNiTi_CMS_2019 | [
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]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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"Ti"
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
[
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AlNi3 | AlNi3 | A3B | [
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| [
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| 2023-12-01T23:19:00 | 12452954455361636064902419016729136643778591485040466864480617157213356337447723834404581305080715328684949369052671703959470602167069287756100104156489041 | CO_1245295445536163606490241 | AlNiTi_CMS_2019 | [
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]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
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"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| 2023-12-01T23:19:00 | 7415613625653550636245403773795732863761514667179685265000229865961388647280817498708254010241700814228491005975373454405802097056852759729273664832602590 | CO_7415613625653550636245403 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 2023-12-01T23:19:00 | 5396091347601720530153747085991776724844214189561646492851525777474428315271299773219539375366680712182477913797243301161696527206105682714889455731873848 | CO_5396091347601720530153747 | AlNiTi_CMS_2019 | [
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| 2023-12-01T23:19:00 | 13093603617430370878244893167883113794454446234775972586258605378760255300684412172725478183330791893120184842248263958548145064287419051588182566708460561 | CO_1309360361743037087824489 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| [
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| 2023-12-01T23:19:00 | 246019172657083892071468611990863867657220672496837033104221979753713482058458360782595673602482856225834842314404905973423820860015274974737616050591873 | CO_2460191726570838920714686 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
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"Gus L.W. Hart",
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| 2023-12-01T23:19:00 | 5451913686786769774457513432549692852517173658863579039625261915391955551187915060132047943889413174402541081759780502097787173553952942970456417901795627 | CO_5451913686786769774457513 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 2023-12-01T23:19:00 | 7570893387428208727506529581538921509162460521015834139542910813268527463977904661828669004667471690808571994539663144639036100380132898455178971059249884 | CO_7570893387428208727506529 | AlNiTi_CMS_2019 | [
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]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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Al5NiTi2 | Al5NiTi2 | A5B2C | [
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| [
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| 2023-12-01T23:19:00 | 151439513125914210217751660712796061874952846469536672113791826003371714715687971533586545292913012854676709280944516904778652902963533862033200120132349 | CO_1514395131259142102177516 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| 2023-12-01T23:19:00 | 1568779297686093635736596394407049511882238844932932729554999424058340674611557260434320942457059035463749251331172176117334689212703533507126802612179379 | CO_1568779297686093635736596 | AlNiTi_CMS_2019 | [
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| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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| [
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| 2023-12-01T23:19:00 | 8492798505929808201501385223037264625926410423830078184867911978658210496070289673389184330575814095104373167499816299703853671666652765985036878775990250 | CO_8492798505929808201501385 | AlNiTi_CMS_2019 | [
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]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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Al3Ti5 | Al3Ti5 | A5B3 | [
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22
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| [
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| 2 | 8 | [
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| 2023-12-01T23:19:00 | 10620517315573995232085897795025746393386738611578297113804387207636022835579853207699347084859361970198428545600146099786215515239964650741525620885419472 | CO_1062051731557399523208589 | AlNiTi_CMS_2019 | [
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]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
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Ni3Ti5 | Ni3Ti5 | A5B3 | [
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22
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| [
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| 2023-12-01T23:19:00 | 989826930519571591394722399091441470534313806695691613481677126233217968200511282902255597610244174785362043721153446545128671173066550629611847423070591 | CO_9898269305195715913947223 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
"Evgeny V. Podryabinkin",
"Gus L.W. Hart",
"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
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"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.2966449914229864,
0.3824550205449396,
0.32089998803207403
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1016/j.commatsci.2018.09.031', 'source-data': 'https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data', 'other': None} | 10.60732/7b56ca82 | 3389360456198058392034570193391541356928879716971205247655861353037594036502163554880877085823897366490724947053850262613353186244287689034639073555582445 | DS_dtjyh96dypuu_0 | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0 |
Ni4Ti6 | Ni2Ti3 | A3B2 | [
28,
28,
28,
28,
22,
22,
22,
22,
22,
22
]
| [
"Ni",
"Ti"
]
| [
0.4,
0.6
]
| 2 | 10 | [
[
-0.075362,
-4.93377,
-0.093203
],
[
2.3091,
-0.11072,
4.19053
],
[
-4.14733,
-2.49813,
4.94785
]
]
| [
[
1.73899,
-0.51477,
4.76667
],
[
-0.31807,
-3.98587,
4.4383
],
[
-2.34342,
-5.38011,
4.14915
],
[
-1.47263,
-3.9305,
2.34303
],
[
-0.30685,
-2.73143,
0.36764
],
[
-2.60238,
-2.77098,
4.36298
],
[
1.20382,
-4.23157,
2.22738
],
[
-1.84803,
-4.26766,
6.63924
],
[
-0.19148,
-1.59298,
3.06768
],
[
-0.37232,
-1.64223,
5.89839
]
]
| [
true,
true,
true
]
| [
1,
1,
1
]
| 3 | 1027232805731977271606537927430027776281206523451429763912033340181315937882942524953812984580435220880515129989885941863894156780873114003144984937925246 | 1 | VASP | DFT | null | [
[
0.120042,
0.055646,
-0.062526
],
[
-0.003573,
-0.097182,
-0.002423
],
[
-0.11328,
0.059048,
0.069881
],
[
-0.001053,
-0.0959,
-0.003314
],
[
-0.127763,
-0.114927,
0.064165
],
[
0.121921,
-0.114739,
-0.072076
],
[
-0.086816,
0.08014,
0.051651
],
[
0.089404,
0.079164,
-0.045341
],
[
0.046883,
0.07478,
-0.025888
],
[
-0.045766,
0.073969,
0.025871
]
]
| null | [
[
-0.0006478686472666822,
0.0001289495785407481,
0.001548268753766601
],
[
0.0001289495785407481,
-0.0020650657093897345,
0.00006584792127806837
],
[
0.001548268753766601,
0.00006584792127806837,
0.0010909533801131347
]
]
| true | null | null | -71.877015 | null | 0.129031 | 0.183436 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | 2024-08-16T15:39:26 | 7010918912162565902368335648033414022115608225205914659967901511170735957448065889516125801220731128021106233606678225650577168580285577861137554664926207 | PO_7010918912162565902368335 | null | null | null | [
"train_1st_stage_666"
]
| [
"DS_dtjyh96dypuu_0"
]
| 2023-12-01T23:19:00 | 9407610597892660227696382046197143357531696329936034994551442920245659945761291115376086174581757582570047524420441990185775303768971648116809159490463618 | CO_9407610597892660227696382 | AlNiTi_CMS_2019 | [
"Konstantin Gubaev",
"Evgeny V. Podryabinkin",
"Gus L.W. Hart",
"Alexander V. Shapeev"
]
| This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. | [
"Al",
"Ni",
"Ti"
]
| 3 | 2,684 | 2,684 | 25,067 | 0 | 2,684 | 0 | 2,684 | 0 | 2,684 | -51.590086 | 906.604882 | 0 | 2023-12-01T18:19:28 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
0.2966449914229864,
0.3824550205449396,
0.32089998803207403
]
| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1016/j.commatsci.2018.09.031', 'source-data': 'https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data', 'other': None} | 10.60732/7b56ca82 | 3389360456198058392034570193391541356928879716971205247655861353037594036502163554880877085823897366490724947053850262613353186244287689034639073555582445 | DS_dtjyh96dypuu_0 | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0 |
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