chemical_formula_hill
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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
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string | dataset_name
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NiTi9
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NiTi9
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A9B
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[
"Ni",
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VASP
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DFT
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MD_1086845055867124744315202
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PO_1096341132415912367552823
| null | null | null |
[
"train_1st_stage_979"
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[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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CO_1321796639458557931776583
|
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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10.60732/7b56ca82
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|
DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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AlNiTi2
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AlNiTi2
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A2BC
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[
"Al",
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"Ti"
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[
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VASP
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DFT
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|
MD_1086845055867124744315202
| 2024-08-16T15:36:20 |
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PO_6403506204373268326632028
| null | null | null |
[
"train_1st_stage_1163"
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[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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|
CO_1192141442076993582402803
|
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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2023
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[
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CC-BY-4.0
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{'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
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
Al2Ti4
|
AlTi2
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A2B
|
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22
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[
"Al",
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[
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[
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] |
[
true,
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[
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] | 3 |
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| 1 |
VASP
|
DFT
| null |
[
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[
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] | true | null | null | -39.946236 | null | 0.432367 | 0.752163 |
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|
MD_1086845055867124744315202
| 2024-08-16T14:23:17 |
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|
PO_2117491222758400019730383
| null | null | null |
[
"train_2nd_stage_263"
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[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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|
CO_1281906476285768634998407
|
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
|
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
Al2Ni6
|
AlNi3
|
A3B
|
[
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13,
28,
28,
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28,
28,
28
] |
[
"Al",
"Ni"
] |
[
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[
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] |
[
true,
true,
true
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[
1,
1,
1
] | 3 |
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| 1 |
VASP
|
DFT
| null |
[
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[
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] | null |
[
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] | true | null | null | -43.510484 | null | 0.428689 | 0.639545 |
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|
MD_1086845055867124744315202
| 2024-08-16T15:33:04 |
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|
PO_8216960166211787100165135
| null | null | null |
[
"train_2nd_stage_566"
] |
[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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|
CO_4682421710433070526363005
|
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
|
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
AlTi3
|
AlTi3
|
A3B
|
[
13,
22,
22,
22
] |
[
"Al",
"Ti"
] |
[
0.25,
0.75
] | 2 | 4 |
[
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[
2.857571,
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[
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] |
[
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DFT
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MD_1086845055867124744315202
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[
"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"
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10.60732/7b56ca82
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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VASP
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DFT
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MD_1086845055867124744315202
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[
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[
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AlNiTi_CMS_2019
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[
"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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{'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
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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NiTi3
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NiTi3
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VASP
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DFT
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MD_1086845055867124744315202
| 2024-08-16T15:02:31 |
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PO_4934361087019327644846729
| null | null | null |
[
"train_2nd_stage_384"
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[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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|
CO_1555326006695412409321255
|
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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2023
|
[
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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
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|
DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
Al2Ti2
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AlTi
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AB
|
[
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22
] |
[
"Al",
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] |
[
0.5,
0.5
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[
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[
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| 1 |
VASP
|
DFT
| null |
[
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[
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MD_1086845055867124744315202
| 2024-08-16T15:41:44 |
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PO_1062350788665484142303178
| null | null | null |
[
"train_1st_stage_2385"
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[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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|
CO_7141581319042300565684752
|
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
|
[
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CC-BY-4.0
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{'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
|
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
Ni4Ti4
|
NiTi
|
AB
|
[
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28,
28,
28,
22,
22,
22,
22
] |
[
"Ni",
"Ti"
] |
[
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[
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] |
[
true,
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true
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[
1,
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1
] | 3 |
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| 1 |
VASP
|
DFT
| null |
[
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[
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|
MD_1086845055867124744315202
| 2024-08-16T15:27:46 |
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|
PO_1230686447823879720624996
| null | null | null |
[
"train_2nd_stage_669"
] |
[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
1708351011882320011701812642208655693916762006256939875113980711497821314163502353667701031358547163121556880774561985161097344750164949726736514616760394
|
CO_1708351011882320011701812
|
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
|
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
Al2Ti4
|
AlTi2
|
A2B
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[
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VASP
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DFT
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MD_1086845055867124744315202
| 2024-08-16T14:47:24 |
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PO_8456520231019466947946148
| null | null | null |
[
"train_1st_stage_2074"
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[
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CO_1052873645126781387629151
|
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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CC-BY-4.0
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{'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
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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AlNi2
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AlNi2
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A2B
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[
"Al",
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| 1 |
VASP
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DFT
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MD_1086845055867124744315202
| 2024-08-16T15:08:30 |
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PO_9458660316271939961990297
| null | null | null |
[
"train_1st_stage_826"
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[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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|
CO_9900461048608511757308642
|
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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2023
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[
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CC-BY-4.0
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{'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
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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AlNi10Ti
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AlNi10Ti
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A10BC
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22
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[
"Al",
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[
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[
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[
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| 1 |
VASP
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DFT
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|
MD_1086845055867124744315202
| 2024-08-16T15:34:06 |
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|
PO_6931601282407846295995730
| null | null | null |
[
"train_1st_stage_323"
] |
[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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|
CO_8513035854194685954434893
|
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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2023
|
[
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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
|
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
Al5Ni2Ti
|
Al5Ni2Ti
|
A5B2C
|
[
13,
13,
13,
13,
13,
28,
28,
22
] |
[
"Al",
"Ni",
"Ti"
] |
[
0.625,
0.25,
0.125
] | 3 | 8 |
[
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[
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[
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3.46993
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[
0,
0,
0
]
] |
[
true,
true,
true
] |
[
1,
1,
1
] | 3 |
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| 1 |
VASP
|
DFT
| null |
[
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[
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] | null |
[
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0.00005729705377560831,
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] | true | null | null | -41.764028 | null | 0.278798 | 0.463893 |
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|
MD_1086845055867124744315202
| 2024-08-16T15:40:28 |
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|
PO_1259851923996795838163618
| null | null | null |
[
"train_2nd_stage_131"
] |
[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
7241772542350279150010962023643597481212138311137495119041995748396146630904156507408407221690312532109032623825762280734090043923330531496160088196203687
|
CO_7241772542350279150010962
|
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
|
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
Al3Ni6Ti3
|
AlNi2Ti
|
A2BC
|
[
13,
13,
13,
28,
28,
28,
28,
28,
28,
22,
22,
22
] |
[
"Al",
"Ni",
"Ti"
] |
[
0.25,
0.5,
0.25
] | 3 | 12 |
[
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[
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6.9619,
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],
[
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]
] |
[
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[
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]
] |
[
true,
true,
true
] |
[
1,
1,
1
] | 3 |
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| 1 |
VASP
|
DFT
| null |
[
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MD_1086845055867124744315202
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[
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[
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"Alexander V. Shapeev"
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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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[
"Al",
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"Ti"
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MD_1086845055867124744315202
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[
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[
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[
"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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10.60732/7b56ca82
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Al3Ni5
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Al3Ni5
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MD_1086845055867124744315202
| 2024-08-16T14:51:09 |
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PO_3714264510021108393001904
| null | null | null |
[
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[
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CO_8834051653006187930976533
|
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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{'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
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|
DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
Al2Ni7Ti
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Al2Ni7Ti
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A7B2C
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[
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[
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VASP
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DFT
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MD_1086845055867124744315202
| 2024-08-16T15:02:18 |
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PO_8913590421592506635609862
| null | null | null |
[
"train_1st_stage_1160"
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[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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|
CO_5143902805681655423315923
|
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
|
[
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{'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
|
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
Al6Ti4
|
Al3Ti2
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A3B2
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[
"Al",
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[
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[
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VASP
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DFT
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MD_1086845055867124744315202
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[
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[
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CO_4460134059113145186257718
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AlNiTi_CMS_2019
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[
"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.
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[
"Al",
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"Ti"
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DS_dtjyh96dypuu_0
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MD_1086845055867124744315202
| 2024-08-16T15:22:12 |
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PO_4985686625135589732036887
| null | null | null |
[
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[
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CO_2846420580790404631875796
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AlNiTi_CMS_2019
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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.
|
[
"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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10.60732/7b56ca82
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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AlNi3Ti2
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AlNi3Ti2
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[
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VASP
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DFT
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MD_1086845055867124744315202
| 2024-08-16T15:38:07 |
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PO_1038681013690639027985860
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[
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[
"DS_dtjyh96dypuu_0"
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|
CO_8538643371010544499085362
|
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
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[
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{'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
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|
DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Al4Ti6
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Al2Ti3
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A3B2
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[
"Al",
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[
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[
true,
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[
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| 1 |
VASP
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DFT
| null |
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|
MD_1086845055867124744315202
| 2024-08-16T15:01:28 |
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PO_6747465414089600240447881
| null | null | null |
[
"train_1st_stage_846"
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[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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|
CO_2358011622794783243099933
|
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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1,
1
]
] |
[
3
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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
|
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
AlNi
|
AlNi
|
AB
|
[
13,
28
] |
[
"Al",
"Ni"
] |
[
0.5,
0.5
] | 2 | 2 |
[
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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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] | 3 |
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| 1 |
VASP
|
DFT
| null |
[
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[
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MD_1086845055867124744315202
| 2024-08-16T14:46:33 |
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PO_7079975719369305907750926
| null | null | null |
[
"train_1st_stage_2351"
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[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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|
CO_6752783363371826435817743
|
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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{'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
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Ni2Ti2
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NiTi
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AB
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VASP
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DFT
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MD_1086845055867124744315202
| 2024-08-16T15:37:32 |
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PO_7790124504410080821234653
| null | null | null |
[
"train_1st_stage_2308"
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[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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|
CO_7625418140094937835270007
|
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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2023
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[
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CC-BY-4.0
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{'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
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Al3Ni7
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Al3Ni7
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A7B3
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28,
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28
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[
"Al",
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[
0.3,
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[
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VASP
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DFT
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|
MD_1086845055867124744315202
| 2024-08-16T15:39:44 |
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PO_3098901699820981571557605
| null | null | null |
[
"train_1st_stage_717"
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[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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|
CO_9042552364440126006743980
|
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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2023
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[
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CC-BY-4.0
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{'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
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
Al2Ni2Ti4
|
AlNiTi2
|
A2BC
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[
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28,
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22,
22,
22,
22
] |
[
"Al",
"Ni",
"Ti"
] |
[
0.25,
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0.5
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[
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[
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[
true,
true,
true
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[
1,
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| 1 |
VASP
|
DFT
| null |
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[
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[
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|
MD_1086845055867124744315202
| 2024-08-16T15:38:09 |
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|
PO_1296752181264767299051235
| null | null | null |
[
"train_2nd_stage_295"
] |
[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
13230723453429357418218617265660546612509795698638200672392534021282126460861319508105968281253936944978490870115379909963465280848170973081035511377524912
|
CO_1323072345342935741821861
|
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
|
Al6Ti4
|
Al3Ti2
|
A3B2
|
[
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13,
13,
22,
22,
22,
22
] |
[
"Al",
"Ti"
] |
[
0.6,
0.4
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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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[
7.58471,
1.63595,
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]
] |
[
true,
true,
true
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[
1,
1,
1
] | 3 |
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| 1 |
VASP
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DFT
| null |
[
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[
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[
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] | true | null | null | -56.916725 | null | 0.490904 | 1.089845 |
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|
MD_1086845055867124744315202
| 2024-08-16T14:25:13 |
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|
PO_9709026989711687521172521
| null | null | null |
[
"train_1st_stage_964"
] |
[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
7544047238001324027276889270983418742151632609766423761173191982786411150264212760132972563515817649964848907769548603244386560952130944254933270609450420
|
CO_7544047238001324027276889
|
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
|
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
Ni5Ti
|
Ni5Ti
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A5B
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[
"Ni",
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VASP
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DFT
| null |
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MD_1086845055867124744315202
| 2024-08-16T14:53:53 |
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PO_1094379311084144437410952
| null | null | null |
[
"train_2nd_stage_195"
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[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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|
CO_2470630878076130406415977
|
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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1
]
] |
[
3
] |
2023
|
[
0.2966449914229864,
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CC-BY-4.0
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{'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
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Ni6Ti10
|
Ni3Ti5
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A5B3
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[
"Ni",
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[
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[
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VASP
|
DFT
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[
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[
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[
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] | true | null | null | -115.076831 | null | 0.178524 | 0.437985 |
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|
MD_1086845055867124744315202
| 2024-08-16T15:30:06 |
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|
PO_7223949641797268390568446
| null | null | null |
[
"train_2nd_stage_6"
] |
[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
6293260837222830607656415319381504470442518504631078555119073081136146498063345132368728383977269827772897229553284267131763043982237722960802709877750039
|
CO_6293260837222830607656415
|
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
|
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
Al8Ti2
|
Al4Ti
|
A4B
|
[
13,
13,
13,
13,
13,
13,
13,
13,
22,
22
] |
[
"Al",
"Ti"
] |
[
0.8,
0.2
] | 2 | 10 |
[
[
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[
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[
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[
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[
4.34791,
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]
] |
[
true,
true,
true
] |
[
1,
1,
1
] | 3 |
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| 1 |
VASP
|
DFT
| null |
[
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[
0.003604,
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[
-0.003266,
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] | null |
[
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] | true | null | null | -48.358947 | null | 0.005092 | 0.009016 |
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|
MD_1086845055867124744315202
| 2024-08-16T14:25:01 |
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|
PO_1327070181794020018019623
| null | null | null |
[
"train_1st_stage_363"
] |
[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
4580524834278851193228413448616300937958796089102288436491921089759947741161601473368783276096743413192123030313330535820860315404375659504027610147338336
|
CO_4580524834278851193228413
|
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
|
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
Al4Ni2Ti2
|
Al2NiTi
|
A2BC
|
[
13,
13,
13,
13,
28,
28,
22,
22
] |
[
"Al",
"Ni",
"Ti"
] |
[
0.5,
0.25,
0.25
] | 3 | 8 |
[
[
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],
[
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0
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[
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] |
[
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[
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[
1.42587,
1.43378,
-12.35025
]
] |
[
true,
true,
true
] |
[
1,
1,
1
] | 3 |
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| 1 |
VASP
|
DFT
| null |
[
[
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[
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0.00001,
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[
-0.000005,
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0.009211
]
] | null |
[
[
0.0008564598822537007,
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[
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[
-3.744905475529955e-7,
3.120754562941629e-7,
0.00048359212707343476
]
] | true | null | null | -45.899188 | null | 0.010492 | 0.022875 |
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|
MD_1086845055867124744315202
| 2024-08-16T15:35:54 |
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|
PO_3343898716522916745994225
| null | null | null |
[
"train_1st_stage_329"
] |
[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
4127672834294160854935964742733384323966633016980554065701267804845668608058243516292349661239163499565389569453662453013584286581247398525483431616213297
|
CO_4127672834294160854935964
|
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
|
Ni5Ti5
|
NiTi
|
AB
|
[
28,
28,
28,
28,
28,
22,
22,
22,
22,
22
] |
[
"Ni",
"Ti"
] |
[
0.5,
0.5
] | 2 | 10 |
[
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MD_1086845055867124744315202
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[
"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"
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{'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}
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DS_dtjyh96dypuu_0
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Al10Ni4
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MD_1086845055867124744315202
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AlNiTi_CMS_2019
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[
"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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{'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}
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Ni12Ti4
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Ni3Ti
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A3B
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[
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MD_1086845055867124744315202
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| null | null | null |
[
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[
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|
CO_1240464102786477433510090
|
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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{'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
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Al2Ni2Ti4
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AlNiTi2
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A2BC
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[
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] |
[
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[
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DFT
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MD_1086845055867124744315202
| 2024-08-16T14:56:33 |
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[
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[
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|
CO_4854170266093743027503464
|
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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2023
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10.60732/7b56ca82
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Al6Ti5
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Al6Ti5
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A6B5
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MD_1086845055867124744315202
| 2024-08-16T14:44:43 |
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PO_1266510094379676956702289
| null | null | null |
[
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[
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CO_1317579134406863702928777
|
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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|
10.60732/7b56ca82
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Al2Ti4
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AlTi2
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A2B
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VASP
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DFT
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MD_1086845055867124744315202
| 2024-08-16T14:43:20 |
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PO_1275740345451532079975822
| null | null | null |
[
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[
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|
CO_8371058757581944153954065
|
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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10.60732/7b56ca82
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Ni5Ti5
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NiTi
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AB
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[
"Ni",
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[
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[
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[
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VASP
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DFT
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[
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|
MD_1086845055867124744315202
| 2024-08-16T14:24:23 |
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PO_1275098533566406328745776
| null | null | null |
[
"train_2nd_stage_660"
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[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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|
CO_6428423221015298471480213
|
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
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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
|
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
Al2Ni12
|
AlNi6
|
A6B
|
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28,
28,
28
] |
[
"Al",
"Ni"
] |
[
0.14285714285714285,
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[
true,
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[
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| 1 |
VASP
|
DFT
| null |
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|
MD_1086845055867124744315202
| 2024-08-16T15:39:00 |
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PO_8813401171378513505499209
| null | null | null |
[
"train_1st_stage_188"
] |
[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
7508607628026458095108528533693362661188043896853256777621187346722997677208125524085294834398239488731082127766796282016806987869387812516063568790080229
|
CO_7508607628026458095108528
|
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",
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10.60732/7b56ca82
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Al3Ni
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A3B
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DFT
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MD_1086845055867124744315202
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[
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[
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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"
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{'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}
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10.60732/7b56ca82
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Al2Ti2
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AB
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[
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VASP
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DFT
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MD_1086845055867124744315202
| 2024-08-16T15:32:56 |
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PO_5301367998074059456523136
| null | null | null |
[
"train_2nd_stage_124"
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[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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CO_2674276351453906720493752
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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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{'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
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Al6Ni4
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Al3Ni2
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A3B2
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[
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VASP
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DFT
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[
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MD_1086845055867124744315202
| 2024-08-16T14:21:27 |
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PO_7014582707315825186754503
| null | null | null |
[
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[
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|
CO_2798938461940932507443178
|
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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2023
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[
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CC-BY-4.0
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{'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
|
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
Al6Ni5Ti
|
Al6Ni5Ti
|
A6B5C
|
[
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22
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[
"Al",
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"Ti"
] |
[
0.5,
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[
true,
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[
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] | 3 |
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| 1 |
VASP
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DFT
| null |
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[
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[
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] | true | null | null | -49.348202 | null | 6.993845 | 34.102619 |
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|
MD_1086845055867124744315202
| 2024-08-16T15:26:57 |
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PO_7448836917927528247672296
| null | null | null |
[
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"train_1st_stage_1860"
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[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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|
CO_7223894660762097800293301
|
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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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
|
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
Al6Ni4
|
Al3Ni2
|
A3B2
|
[
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13,
13,
13,
13,
13,
28,
28,
28,
28
] |
[
"Al",
"Ni"
] |
[
0.6,
0.4
] | 2 | 10 |
[
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[
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[
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DFT
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MD_1086845055867124744315202
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| null | null | null |
[
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[
"DS_dtjyh96dypuu_0"
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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"
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Al6Ni4
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A3B2
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VASP
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DFT
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MD_1086845055867124744315202
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[
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[
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] | 2023-12-01T23:19:00 |
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CO_8252316975995728906189933
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|
[
"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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2023
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[
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{'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
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Al2NiTi
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A2BC
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[
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VASP
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DFT
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MD_1086845055867124744315202
| 2024-08-16T14:57:10 |
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PO_3047813603448070890672573
| null | null | null |
[
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[
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] | 2023-12-01T23:19:00 |
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|
CO_4420054690121134878824913
|
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
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[
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CC-BY-4.0
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{'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
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Al4Ni4Ti4
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AlNiTi
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ABC
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22
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[
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[
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[
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[
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| 1 |
VASP
|
DFT
| null |
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|
MD_1086845055867124744315202
| 2024-08-16T14:56:07 |
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PO_8184960481168720466019405
| null | null | null |
[
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"train_1st_stage_1808",
"train_1st_stage_2008"
] |
[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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|
CO_5469214009572652603631805
|
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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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
|
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
AlNiTi
|
AlNiTi
|
ABC
|
[
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28,
22
] |
[
"Al",
"Ni",
"Ti"
] |
[
0.3333333333333333,
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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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] |
[
true,
true,
true
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[
1,
1,
1
] | 3 |
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| 1 |
VASP
|
DFT
| null |
[
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MD_1086845055867124744315202
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PO_3417288600325053693563559
| null | null | null |
[
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[
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CO_1242319396267618765643586
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AlNiTi_CMS_2019
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[
"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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[
"Al",
"Ni",
"Ti"
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10.60732/7b56ca82
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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AlNiTi4
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AlNiTi4
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MD_1086845055867124744315202
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AlNiTi_CMS_2019
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[
"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"
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{'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}
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Ni4Ti12
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NiTi3
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MD_1086845055867124744315202
| 2024-08-16T14:21:54 |
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| null | null | null |
[
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[
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CO_2841523758151888993235712
|
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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[
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2023
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[
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{'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
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Al2Ni
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Al2Ni
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A2B
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[
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[
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VASP
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DFT
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MD_1086845055867124744315202
| 2024-08-16T15:13:41 |
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PO_5451847352355976591034242
| null | null | null |
[
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[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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|
CO_6678023242195715137687344
|
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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2023
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[
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CC-BY-4.0
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{'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
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
AlNi5Ti4
|
AlNi5Ti4
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A5B4C
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[
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[
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[
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[
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VASP
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DFT
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MD_1086845055867124744315202
| 2024-08-16T14:41:49 |
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PO_8724535792538621480830012
| null | null | null |
[
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[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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CO_1051959570010436694234916
|
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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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
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DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Ni2Ti2
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NiTi
|
AB
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[
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28,
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22
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[
"Ni",
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0.5,
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VASP
|
DFT
| null |
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|
MD_1086845055867124744315202
| 2024-08-16T15:03:55 |
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PO_4876906291726744307000238
| null | null | null |
[
"train_2nd_stage_320"
] |
[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
2957592756352297600300850563288951739816213008759610300467021269808737836604432957150995246618030048903589176870364620348891084324153700535108679582110476
|
CO_2957592756352297600300850
|
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
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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
|
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
Al4Ti5
|
Al4Ti5
|
A5B4
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[
13,
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22,
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22,
22,
22
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[
"Al",
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[
0.4444444444444444,
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[
true,
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true
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[
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| 1 |
VASP
|
DFT
| null |
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[
-0.052392,
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[
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|
MD_1086845055867124744315202
| 2024-08-16T15:40:38 |
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PO_1100595026567995577043762
| null | null | null |
[
"train_2nd_stage_615"
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[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
6812481447824055947324728069681130922113985338059735819878172987329767438390129183386708902924929261494156925287787454047171372469415088437153112923434274
|
CO_6812481447824055947324728
|
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
|
Al11Ti
|
Al11Ti
|
A11B
|
[
13,
13,
13,
13,
13,
13,
13,
13,
13,
13,
13,
22
] |
[
"Al",
"Ti"
] |
[
0.9166666666666666,
0.08333333333333333
] | 2 | 12 |
[
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[
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[
true,
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true
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[
1,
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| 1 |
VASP
|
DFT
| null |
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[
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[
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] | true | null | null | -49.939688 | null | 0.155899 | 0.240713 |
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|
MD_1086845055867124744315202
| 2024-08-16T14:50:42 |
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|
PO_1159094841383852448936461
| null | null | null |
[
"train_1st_stage_607"
] |
[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
1999176155058278240986095308841963958486074088426583763663976985193969172129270851270328176910262245438491516087044029534350850917014856978082544928078419
|
CO_1999176155058278240986095
|
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
|
Al6Ni5Ti
|
Al6Ni5Ti
|
A6B5C
|
[
13,
13,
13,
13,
13,
13,
28,
28,
28,
28,
28,
22
] |
[
"Al",
"Ni",
"Ti"
] |
[
0.5,
0.4166666666666667,
0.08333333333333333
] | 3 | 12 |
[
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[
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[
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[
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[
true,
true,
true
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[
1,
1,
1
] | 3 |
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| 1 |
VASP
|
DFT
| null |
[
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[
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[
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0.0029297019685983424,
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] | true | null | null | -50.023072 | null | 6.44568 | 32.427089 |
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|
MD_1086845055867124744315202
| 2024-08-16T15:03:31 |
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|
PO_1321270987078083983568868
| null | null | null |
[
"train_1st_stage_1750",
"train_1st_stage_1950",
"train_1st_stage_1350",
"train_1st_stage_1550"
] |
[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
10411921057460810367449345825302839061161584304781289600626118782817772766931717976622247567008412879473006249958399217278730140250970863020446090528012201
|
CO_1041192105746081036744934
|
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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2023
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[
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{'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
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Al3Ni
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VASP
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DFT
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MD_1086845055867124744315202
| 2024-08-16T15:17:02 |
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PO_3413445755713726380610205
| null | null | null |
[
"train_2nd_stage_48"
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[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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CO_1129654524308298349063703
|
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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2023
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[
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CC-BY-4.0
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{'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
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Al3Ti
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Al3Ti
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A3B
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[
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13,
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22
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[
"Al",
"Ti"
] |
[
0.75,
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[
true,
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[
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| 1 |
VASP
|
DFT
| null |
[
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|
MD_1086845055867124744315202
| 2024-08-16T14:29:59 |
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PO_3915529655725026484233065
| null | null | null |
[
"train_2nd_stage_400"
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[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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|
CO_5615861565802703323370721
|
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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2023
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[
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CC-BY-4.0
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{'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
|
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Al3Ni6Ti3
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AlNi2Ti
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A2BC
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[
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[
"Al",
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"Ti"
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[
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[
true,
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true
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[
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1
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| 1 |
VASP
|
DFT
| null |
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[
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[
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|
MD_1086845055867124744315202
| 2024-08-16T15:33:40 |
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|
PO_8340588706593300774933425
| null | null | null |
[
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"train_1st_stage_1948",
"train_1st_stage_1348",
"train_1st_stage_1548"
] |
[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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|
CO_9150804981443823486903229
|
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
|
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
Al6Ni2Ti4
|
Al3NiTi2
|
A3B2C
|
[
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13,
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13,
13,
13,
28,
28,
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22,
22,
22
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[
"Al",
"Ni",
"Ti"
] |
[
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[
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[
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[
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[
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[
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[
true,
true,
true
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[
1,
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1
] | 3 |
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| 1 |
VASP
|
DFT
| null |
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[
1.856446,
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] | null |
[
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] | true | null | null | -31.79516 | null | 14.219063 | 35.599073 |
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|
MD_1086845055867124744315202
| 2024-08-16T15:11:32 |
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|
PO_4035118497487477684650198
| null | null | null |
[
"train_1st_stage_1917",
"train_1st_stage_1717",
"train_1st_stage_1317",
"train_1st_stage_1517"
] |
[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
5289012572564232271226385078894945059772316457986269416632070291990940692273851695874911628723362106539899223650402576949548466084814648703093017283592453
|
CO_5289012572564232271226385
|
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
|
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MD_1086845055867124744315202
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[
"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.
|
[
"Al",
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"Ti"
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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MD_1086845055867124744315202
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CO_5274716039022822749629173
|
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"
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{'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}
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10.60732/7b56ca82
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Al3Ti9
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AlTi3
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MD_1086845055867124744315202
| 2024-08-16T15:36:52 |
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PO_8674253041394781068914171
| null | null | null |
[
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[
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CO_1196553216413148954061456
|
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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{'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
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|
DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Al4Ni8
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AlNi2
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A2B
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[
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DFT
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MD_1086845055867124744315202
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PO_9075898172326045525572137
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[
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[
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|
CO_5664536702672448841988506
|
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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[
"Al",
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"Ti"
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10.60732/7b56ca82
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Al7Ni3Ti2
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MD_1086845055867124744315202
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PO_1147342707743982810816067
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CO_4965705735837586680127157
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[
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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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|
10.60732/7b56ca82
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Al8Ti6
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Al4Ti3
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A4B3
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MD_1086845055867124744315202
| 2024-08-16T15:41:02 |
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PO_2208003958415460981073725
| null | null | null |
[
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[
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CO_4825860962505735513832606
|
AlNiTi_CMS_2019
|
[
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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"
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{'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
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
Al3Ni8Ti
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Al3Ni8Ti
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A8B3C
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[
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MD_1086845055867124744315202
| 2024-08-16T15:24:20 |
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PO_2188203186151305061363549
| null | null | null |
[
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[
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] | 2023-12-01T23:19:00 |
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|
CO_5476960568533433541934234
|
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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{'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
|
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
Al5Ni3Ti4
|
Al5Ni3Ti4
|
A5B4C3
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[
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VASP
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DFT
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MD_1086845055867124744315202
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[
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[
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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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DS_dtjyh96dypuu_0
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MD_1086845055867124744315202
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PO_1147956473788064442852626
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[
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[
"DS_dtjyh96dypuu_0"
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CO_1115702843078464741122872
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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"
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DS_dtjyh96dypuu_0
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MD_1086845055867124744315202
| 2024-08-16T15:02:36 |
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PO_2748723315869956755110732
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[
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[
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] | 2023-12-01T23:19:00 |
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|
CO_6878145411826442790981501
|
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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[
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{'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
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|
DS_dtjyh96dypuu_0
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VASP
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DFT
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MD_1086845055867124744315202
| 2024-08-16T15:34:31 |
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| null | null | null |
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[
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CO_5795169901575448640388362
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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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[
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{'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
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Ni9Ti5
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A9B5
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MD_1086845055867124744315202
| 2024-08-16T14:54:27 |
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PO_4592930392581118904959999
| null | null | null |
[
"train_1st_stage_1180"
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[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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|
CO_7086370909170532102870491
|
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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MD_1086845055867124744315202
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[
"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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DS_dtjyh96dypuu_0
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MD_1086845055867124744315202
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[
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CO_1304049970346996612806865
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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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"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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{'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
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Al11Ti
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Al11Ti
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A11B
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[
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MD_1086845055867124744315202
| 2024-08-16T15:01:39 |
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PO_9588614664244959480262962
| null | null | null |
[
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[
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] | 2023-12-01T23:19:00 |
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|
CO_1280007007114693964457239
|
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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{'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
|
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|
DS_dtjyh96dypuu_0
|
AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
|
Al5Ni4Ti3
|
Al5Ni4Ti3
|
A5B4C3
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[
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[
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[
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VASP
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DFT
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|
MD_1086845055867124744315202
| 2024-08-16T14:41:39 |
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[
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[
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] | 2023-12-01T23:19:00 |
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CO_6910697309676422433939964
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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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10.60732/7b56ca82
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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MD_1086845055867124744315202
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[
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CO_1547445810375996944856102
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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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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Ni5Ti7
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Ni5Ti7
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A7B5
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MD_1086845055867124744315202
| 2024-08-16T14:37:12 |
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[
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CO_2282373012823008423602041
|
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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{'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
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Ni4Ti7
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Ni4Ti7
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A7B4
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MD_1086845055867124744315202
| 2024-08-16T14:23:04 |
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PO_7217309209495310084956273
| null | null | null |
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[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
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|
CO_2245363342480149857912062
|
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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{'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
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DS_dtjyh96dypuu_0
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AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_0
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Al2Ni2Ti6
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AlNiTi3
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A3BC
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VASP
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0.010245,
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[
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] | null |
[
[
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[
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[
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0.000017351395369955457,
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] | true | null | null | -68.667935 | null | 0.009744 | 0.022854 |
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|
MD_1086845055867124744315202
| 2024-08-16T15:00:15 |
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|
PO_3886479540307460848877163
| null | null | null |
[
"train_1st_stage_302"
] |
[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
6808573792570663600662156592652724911918471408225390477499411432481649489988424111186565895609763959220678543617589111359280970978891036713796265612944908
|
CO_6808573792570663600662156
|
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
|
NiTi5
|
NiTi5
|
A5B
|
[
28,
22,
22,
22,
22,
22
] |
[
"Ni",
"Ti"
] |
[
0.16666666666666666,
0.8333333333333334
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[
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[
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[
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[
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[
-1.85672,
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0
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[
-1.85672,
4.11416,
2.2723
]
] |
[
true,
true,
true
] |
[
1,
1,
1
] | 3 |
116918891501666944280257929633476328068406428097586986679245562556157852005936759546998228581821143587633028029292385549026001498569035422764152847417950
| 1 |
VASP
|
DFT
| null |
[
[
-0.000002,
0.000554,
0.000009
],
[
0.000004,
0.383774,
-0.000005
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[
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],
[
-0.000001,
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0.000008
],
[
0.000008,
-0.384856,
-0.00001
]
] | null |
[
[
0.0009988287054150979,
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0
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[
0,
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6.241509125883259e-8
],
[
0,
6.241509125883259e-8,
0.022064858231639974
]
] | true | null | null | -44.535932 | null | 0.162531 | 0.384856 |
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|
MD_1086845055867124744315202
| 2024-08-16T14:38:22 |
9189631829283782483275880536859582355326990716786642488442503084657460663112877562560795043977363995772060247733545646106042319892853640435538100866641943
|
PO_9189631829283782483275880
| null | null | null |
[
"train_2nd_stage_229"
] |
[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
10832993820735788826491952191698079299349972984697904668481494839210948463477996111853866116385701339154304729591252668791657141469393065780850646981819813
|
CO_1083299382073578882649195
|
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
|
AlNi3
|
AlNi3
|
A3B
|
[
13,
28,
28,
28
] |
[
"Al",
"Ni"
] |
[
0.25,
0.75
] | 2 | 4 |
[
[
0,
-2.09666,
-2.09666
],
[
0,
-2.09666,
2.09666
],
[
-5.1939,
0,
0
]
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[
[
0,
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0
],
[
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0
],
[
-2.59695,
-0.00001,
0
],
[
-1.30406,
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0
]
] |
[
true,
true,
true
] |
[
1,
1,
1
] | 3 |
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| 1 |
VASP
|
DFT
| null |
[
[
0.000378,
0.000069,
0.000001
],
[
0.219482,
-0.00007,
0
],
[
-0.000343,
0.00007,
0
],
[
-0.219518,
-0.000069,
0
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] | null |
[
[
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0,
0
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[
0,
0.0017043688970049412,
0
],
[
0,
0,
0.0017043688970049412
]
] | true | null | null | -21.407256 | null | 0.109934 | 0.219518 |
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|
MD_1086845055867124744315202
| 2024-08-16T15:24:17 |
9764033529250853974479121817973753752754819859591700288015003725431840364692921771971369544613455349752771206277651472756849309978360988910295955785672360
|
PO_9764033529250853974479121
| null | null | null |
[
"train_2nd_stage_590"
] |
[
"DS_dtjyh96dypuu_0"
] | 2023-12-01T23:19:00 |
4579731223232378788782426689277973738188765100251864888832460987130131716723493855152903696852614004422228599733131910838247585167572937523666432930200998
|
CO_4579731223232378788782426
|
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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