chemical_formula_hill
string | chemical_formula_reduced
string | chemical_formula_anonymous
string | atomic_numbers
list | elements
list | elements_ratios
list | nelements
int32 | nsites
int32 | cell
list | positions
list | pbc
list | dimension_types
list | nperiodic_dimensions
int32 | structure_hash
string | multiplicity
int32 | software
string | method
string | adsorption_energy
float64 | atomic_forces
list | atomization_energy
float64 | cauchy_stress
list | cauchy_stress_volume_normalized
bool | electronic_band_gap
float64 | electronic_band_gap_type
string | energy
float64 | formation_energy
float64 | max_force_norm
float64 | mean_force_norm
float64 | property_object_metadata
string | property_object_metadata_id
string | property_object_last_modified
timestamp[ns] | property_object_hash
string | property_object_id
string | configuration_metadata
string | configuration_metadata_id
string | configuration_labels
list | configuration_names
list | configuration_dataset_ids
list | configuration_last_modified
timestamp[ns] | configuration_hash
string | configuration_id
string | dataset_name
string | dataset_authors
list | dataset_description
string | dataset_elements
list | dataset_nelements
int32 | dataset_nproperty_objects
int64 | dataset_nconfigurations
int32 | dataset_nsites
int64 | dataset_adsorption_energy_count
int64 | dataset_atomic_forces_count
int64 | dataset_atomization_energy_count
int64 | dataset_cauchy_stress_count
int64 | dataset_electronic_band_gap_count
int64 | dataset_energy_count
int64 | dataset_energy_mean
float64 | dataset_energy_variance
float64 | dataset_formation_energy_count
int64 | dataset_last_modified
timestamp[ns] | dataset_dimension_types
list | dataset_nperiodic_dimensions
list | dataset_publication_year
string | dataset_total_elements_ratios
list | dataset_license
string | dataset_links
string | dataset_doi
string | dataset_hash
string | dataset_id
string | dataset_extended_id
string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Ag20Au12
|
Ag5Au3
|
A5B3
|
[
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[
"Ag",
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[
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[
true,
true,
true
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[
1,
1,
1
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| 1 |
VASP
|
DFT-PBE-D3
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] | null |
[
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] | true | null | null | -105.952369 | null | 0.040036 | 0.078053 |
{"input": {"encut": {"value": 650, "units": "eV"}}, "hash": "9985399391223336507104240349147387433243557514633906439105331955381975213318101075814762881954519768656981458682509405813528289710302917162391265942695039", "id": "MD_9985399391223336507104240"}
|
MD_9985399391223336507104240
| 2024-08-16T14:36:36 |
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|
PO_8381380142366045677653429
| null | null | null |
[
"deepmd_set.000_108"
] |
[
"DS_t0ec1irmeo59_0"
] | 2023-12-01T23:16:22 |
10174113338449037923671383251109650139813020950510147970448746862289827759176586465618031685585148738120248905377627194525342417314552642087009135246852820
|
CO_1017411333844903792367138
|
AgAu-nanoalloy_MSMSE_2021
|
[
"Yinan Wang",
"Xiaoyang Wang",
"Linfeng Zhang",
"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
"Ag",
"Au"
] | 2 | 51,766 | 51,771 | 1,188,220 | 0 | 51,766 | 0 | 51,766 | 0 | 51,766 | -68.544468 | 1,402.940203 | 0 | 2023-12-01T18:18:50 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.45979784888320346,
0.5402021511167966
] |
LGPL-3.0-only
|
{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
|
10.60732/a222a0f6
|
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|
DS_t0ec1irmeo59_0
|
AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
|
Ag29Au3
|
Ag29Au3
|
A29B3
|
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47,
47,
47,
47,
47,
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47,
47,
47,
47,
79,
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79
] |
[
"Ag",
"Au"
] |
[
0.90625,
0.09375
] | 2 | 32 |
[
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0
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] |
[
true,
true,
true
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[
1,
1,
1
] | 3 |
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| 1 |
VASP
|
DFT-PBE-D3
| null |
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MD_9985399391223336507104240
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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MD_9985399391223336507104240
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MD_9985399391223336507104240
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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MD_9985399391223336507104240
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[
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Ag8Au8
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AgAu
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AB
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MD_9985399391223336507104240
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PO_1141500396153579069149040
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[
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CO_1017864940242242102938737
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AgAu-nanoalloy_MSMSE_2021
|
[
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"Ben Xu",
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
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2023
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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10.60732/a222a0f6
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DS_t0ec1irmeo59_0
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Au16
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MD_9985399391223336507104240
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MD_9985399391223336507104240
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CO_1017935088243245222509519
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[
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Ag27Au5
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MD_9985399391223336507104240
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PO_6699845902968274503508089
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[
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[
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CO_1017988023765741859355821
|
AgAu-nanoalloy_MSMSE_2021
|
[
"Yinan Wang",
"Xiaoyang Wang",
"Linfeng Zhang",
"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
"Ag",
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] | 2 | 51,766 | 51,771 | 1,188,220 | 0 | 51,766 | 0 | 51,766 | 0 | 51,766 | -68.544468 | 1,402.940203 | 0 | 2023-12-01T18:18:50 |
[
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[
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2023
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[
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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10.60732/a222a0f6
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag6Au26
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Ag3Au13
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A13B3
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DFT-PBE-D3
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MD_9985399391223336507104240
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PO_8219437310311261955208012
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CO_1018076760697114036865719
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AgAu-nanoalloy_MSMSE_2021
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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[
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Au16
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Au
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A
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VASP
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DFT-PBE-D3
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MD_9985399391223336507104240
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PO_1188724295224107478658697
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[
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CO_1018166544485683551494393
|
AgAu-nanoalloy_MSMSE_2021
|
[
"Yinan Wang",
"Xiaoyang Wang",
"Linfeng Zhang",
"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
"Ag",
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] | 2 | 51,766 | 51,771 | 1,188,220 | 0 | 51,766 | 0 | 51,766 | 0 | 51,766 | -68.544468 | 1,402.940203 | 0 | 2023-12-01T18:18:50 |
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2023
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
|
10.60732/a222a0f6
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag12
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Ag
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A
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[
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MD_9985399391223336507104240
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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Ag24Au8
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DFT-PBE-D3
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MD_9985399391223336507104240
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CO_1018900086715262559000341
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AgAu-nanoalloy_MSMSE_2021
|
[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
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Au16
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Au
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MD_9985399391223336507104240
| 2024-08-16T15:23:32 |
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PO_1286839422839267713000860
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[
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CO_1018922749979648543320233
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AgAu-nanoalloy_MSMSE_2021
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[
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"Ben Xu",
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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[
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2023
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag10Au22
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Ag5Au11
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A11B5
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MD_9985399391223336507104240
| 2024-08-16T15:25:29 |
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CO_1018990882907770341767590
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AgAu-nanoalloy_MSMSE_2021
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag25Au7
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Ag25Au7
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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Ag12
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MD_9985399391223336507104240
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CO_1019237972104274542598384
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[
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DS_t0ec1irmeo59_0
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Au16
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Au
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MD_9985399391223336507104240
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[
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CO_1019467142048600526329117
|
AgAu-nanoalloy_MSMSE_2021
|
[
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"Linfeng Zhang",
"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
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2023
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[
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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10.60732/a222a0f6
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Ag32
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Ag
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MD_9985399391223336507104240
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Ag3Au13
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MD_9985399391223336507104240
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AgAu-nanoalloy_MSMSE_2021
|
[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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2023
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LGPL-3.0-only
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Ag3Au13
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MD_9985399391223336507104240
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CO_1019852081280698477841031
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AgAu-nanoalloy_MSMSE_2021
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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Au16
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MD_9985399391223336507104240
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PO_1389118691955610073626859
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[
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CO_1019930336045787454806626
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AgAu-nanoalloy_MSMSE_2021
|
[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag9Au7
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Ag9Au7
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MD_9985399391223336507104240
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PO_3107995904783740801399249
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CO_1019976581441525652441854
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AgAu-nanoalloy_MSMSE_2021
|
[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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[
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2023
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag15Au17
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Ag15Au17
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A17B15
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AgAu-nanoalloy_MSMSE_2021
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[
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"Ben Xu",
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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DS_t0ec1irmeo59_0
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Ag17Au31
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Ag17Au31
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MD_9985399391223336507104240
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CO_1020077651045568938790536
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AgAu-nanoalloy_MSMSE_2021
|
[
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"Linfeng Zhang",
"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag12Au4
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Ag3Au
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MD_9985399391223336507104240
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PO_5693633072060656978016236
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CO_1020111685524389924289164
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AgAu-nanoalloy_MSMSE_2021
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag8Au8
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AgAu
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AB
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MD_9985399391223336507104240
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PO_3370539807867568957703792
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[
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CO_1020247621111322616384759
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AgAu-nanoalloy_MSMSE_2021
|
[
"Yinan Wang",
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"Linfeng Zhang",
"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
"Ag",
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] | 2 | 51,766 | 51,771 | 1,188,220 | 0 | 51,766 | 0 | 51,766 | 0 | 51,766 | -68.544468 | 1,402.940203 | 0 | 2023-12-01T18:18:50 |
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[
3
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2023
|
[
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
|
10.60732/a222a0f6
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag17Au15
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Ag17Au15
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A17B15
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MD_9985399391223336507104240
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CO_1020490800144066347530929
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AgAu-nanoalloy_MSMSE_2021
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[
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"Linfeng Zhang",
"Ben Xu",
"Han Wang"
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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[
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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10.60732/a222a0f6
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag12Au4
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Ag3Au
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MD_9985399391223336507104240
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[
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CO_1020492365311483965189752
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AgAu-nanoalloy_MSMSE_2021
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[
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"Ben Xu",
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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Ag11Au5
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MD_9985399391223336507104240
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CO_1020624253627590619240890
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AgAu-nanoalloy_MSMSE_2021
|
[
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"Ben Xu",
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag4Au44
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AgAu11
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A11B
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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Ag2Au14
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AgAu7
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A7B
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MD_9985399391223336507104240
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PO_6779671784026975008071346
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CO_1020681565051700877908328
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AgAu-nanoalloy_MSMSE_2021
|
[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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2023
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10.60732/a222a0f6
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DS_t0ec1irmeo59_0
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Au16
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Au
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DFT-PBE-D3
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MD_9985399391223336507104240
| 2024-08-16T15:08:37 |
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PO_7512217853171086682116958
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[
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CO_1020682030618504945702819
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AgAu-nanoalloy_MSMSE_2021
|
[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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[
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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10.60732/a222a0f6
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Ag12Au4
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MD_9985399391223336507104240
| 2024-08-16T15:11:09 |
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PO_4875537580650732238596618
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[
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[
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CO_1020836928250928436723297
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AgAu-nanoalloy_MSMSE_2021
|
[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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2023
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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10.60732/a222a0f6
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DS_t0ec1irmeo59_0
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Ag11Au5
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Ag11Au5
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A11B5
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MD_9985399391223336507104240
| 2024-08-16T14:32:25 |
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PO_1142558704496179694306113
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CO_1020933529757845788735688
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AgAu-nanoalloy_MSMSE_2021
|
[
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"Ben Xu",
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
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2023
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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10.60732/a222a0f6
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3481311511156556191035265306681170184294725465275752597341925511358567169350018029706492209753559675591025445233267163801275321275902414163540327935897054
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag4Au12
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AgAu3
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A3B
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MD_9985399391223336507104240
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AgAu-nanoalloy_MSMSE_2021
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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MD_9985399391223336507104240
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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MD_9985399391223336507104240
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AgAu-nanoalloy_MSMSE_2021
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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Ag7Au25
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CO_1021251837598114879782353
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AgAu-nanoalloy_MSMSE_2021
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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Ag24Au8
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AgAu-nanoalloy_MSMSE_2021
|
[
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] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag22Au10
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Ag11Au5
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[
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MD_9985399391223336507104240
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AgAu-nanoalloy_MSMSE_2021
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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DFT-PBE-D3
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MD_9985399391223336507104240
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PO_1008153088524491436721596
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[
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CO_1021685320195743865264930
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AgAu-nanoalloy_MSMSE_2021
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[
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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Ag7Au9
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Ag7Au9
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A9B7
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VASP
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DFT-PBE-D3
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MD_9985399391223336507104240
| 2024-08-16T15:14:26 |
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PO_4836008991065647860043549
| null | null | null |
[
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[
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CO_1021713823283983176587823
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AgAu-nanoalloy_MSMSE_2021
|
[
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"Ben Xu",
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
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[
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2023
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[
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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10.60732/a222a0f6
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag5Au11
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Ag5Au11
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A11B5
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VASP
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DFT-PBE-D3
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MD_9985399391223336507104240
| 2024-08-16T15:14:26 |
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PO_1142828876840359480136532
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[
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[
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CO_1021764178270216240776959
|
AgAu-nanoalloy_MSMSE_2021
|
[
"Yinan Wang",
"Xiaoyang Wang",
"Linfeng Zhang",
"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
"Ag",
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] | 2 | 51,766 | 51,771 | 1,188,220 | 0 | 51,766 | 0 | 51,766 | 0 | 51,766 | -68.544468 | 1,402.940203 | 0 | 2023-12-01T18:18:50 |
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] |
[
3
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2023
|
[
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LGPL-3.0-only
|
{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
|
10.60732/a222a0f6
|
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|
DS_t0ec1irmeo59_0
|
AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
|
Au16
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Au
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A
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[
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[
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MD_9985399391223336507104240
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AgAu-nanoalloy_MSMSE_2021
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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Au16
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MD_9985399391223336507104240
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PO_1026022569340761574366622
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[
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CO_1021983773960050735052558
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AgAu-nanoalloy_MSMSE_2021
|
[
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"Linfeng Zhang",
"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
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2023
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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10.60732/a222a0f6
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag12Au20
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Ag3Au5
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A5B3
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MD_9985399391223336507104240
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CO_1022008441890552684845774
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AgAu-nanoalloy_MSMSE_2021
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[
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"Ben Xu",
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Au32
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MD_9985399391223336507104240
| 2024-08-16T14:14:18 |
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[
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CO_1022046861005002665081802
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AgAu-nanoalloy_MSMSE_2021
|
[
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"Linfeng Zhang",
"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
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] | 2 | 51,766 | 51,771 | 1,188,220 | 0 | 51,766 | 0 | 51,766 | 0 | 51,766 | -68.544468 | 1,402.940203 | 0 | 2023-12-01T18:18:50 |
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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Au32
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MD_9985399391223336507104240
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AgAu-nanoalloy_MSMSE_2021
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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MD_9985399391223336507104240
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CO_1022386080949823285222589
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AgAu-nanoalloy_MSMSE_2021
|
[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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Ag43Au5
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Ag43Au5
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CO_1022494495939964946149385
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AgAu-nanoalloy_MSMSE_2021
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag9Au23
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Ag9Au23
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A23B9
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MD_9985399391223336507104240
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PO_6047494518818942453634255
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CO_1022568432734204603482842
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AgAu-nanoalloy_MSMSE_2021
|
[
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"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
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2023
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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10.60732/a222a0f6
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag32
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Ag
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A
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DFT-PBE-D3
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MD_9985399391223336507104240
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PO_8953031939974645678156378
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[
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CO_1022585641151822347299615
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AgAu-nanoalloy_MSMSE_2021
|
[
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"Linfeng Zhang",
"Ben Xu",
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
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] | 2 | 51,766 | 51,771 | 1,188,220 | 0 | 51,766 | 0 | 51,766 | 0 | 51,766 | -68.544468 | 1,402.940203 | 0 | 2023-12-01T18:18:50 |
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[
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2023
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag14Au2
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Ag7Au
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A7B
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[
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VASP
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DFT-PBE-D3
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MD_9985399391223336507104240
| 2024-08-16T14:56:33 |
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PO_5276367126795992987117671
| null | null | null |
[
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[
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CO_1022807260389689089980093
|
AgAu-nanoalloy_MSMSE_2021
|
[
"Yinan Wang",
"Xiaoyang Wang",
"Linfeng Zhang",
"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
"Ag",
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] | 2 | 51,766 | 51,771 | 1,188,220 | 0 | 51,766 | 0 | 51,766 | 0 | 51,766 | -68.544468 | 1,402.940203 | 0 | 2023-12-01T18:18:50 |
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] |
[
3
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2023
|
[
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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10.60732/a222a0f6
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Au16
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Au
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A
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[
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[
1
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DFT-PBE-D3
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MD_9985399391223336507104240
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AgAu-nanoalloy_MSMSE_2021
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[
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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Ag5Au11
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MD_9985399391223336507104240
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PO_5789211796095563959253934
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CO_1022976136488105294870785
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AgAu-nanoalloy_MSMSE_2021
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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2023
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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Ag13Au3
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Ag13Au3
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MD_9985399391223336507104240
| 2024-08-16T14:13:47 |
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PO_4667104483983371168840367
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CO_1023247741188746640375429
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AgAu-nanoalloy_MSMSE_2021
|
[
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"Ben Xu",
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
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2023
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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10.60732/a222a0f6
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag7Au9
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Ag7Au9
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A9B7
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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Ag7Au9
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MD_9985399391223336507104240
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CO_1023381008244385551258958
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AgAu-nanoalloy_MSMSE_2021
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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DS_t0ec1irmeo59_0
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Ag13Au19
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Ag13Au19
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MD_9985399391223336507104240
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CO_1023382663695550494973791
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AgAu-nanoalloy_MSMSE_2021
|
[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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10.60732/a222a0f6
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag21Au11
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Ag21Au11
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A21B11
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VASP
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DFT-PBE-D3
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MD_9985399391223336507104240
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PO_2630911383483407443739753
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[
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CO_1023589566121862793360246
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AgAu-nanoalloy_MSMSE_2021
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[
"Yinan Wang",
"Xiaoyang Wang",
"Linfeng Zhang",
"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
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] | 2 | 51,766 | 51,771 | 1,188,220 | 0 | 51,766 | 0 | 51,766 | 0 | 51,766 | -68.544468 | 1,402.940203 | 0 | 2023-12-01T18:18:50 |
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2023
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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10.60732/a222a0f6
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Au12
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Au
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A
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VASP
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DFT-PBE-D3
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MD_9985399391223336507104240
| 2024-08-16T15:25:34 |
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PO_7917047270337645232403035
| null | null | null |
[
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[
"DS_t0ec1irmeo59_0",
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] | 2023-12-01T23:25:27 |
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CO_1023893475094923828835224
|
AgAu-nanoalloy_MSMSE_2021
|
[
"Yinan Wang",
"Xiaoyang Wang",
"Linfeng Zhang",
"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
"Ag",
"Au"
] | 2 | 51,766 | 51,771 | 1,188,220 | 0 | 51,766 | 0 | 51,766 | 0 | 51,766 | -68.544468 | 1,402.940203 | 0 | 2023-12-01T18:18:50 |
[
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] |
[
3
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2023
|
[
0.45979784888320346,
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LGPL-3.0-only
|
{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
|
10.60732/a222a0f6
|
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|
DS_t0ec1irmeo59_0
|
AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
|
Ag19Au29
|
Ag19Au29
|
A29B19
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[
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MD_9985399391223336507104240
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CO_1023942709668809155630267
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AgAu-nanoalloy_MSMSE_2021
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag15Au17
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MD_9985399391223336507104240
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CO_1023955632035522488440784
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AgAu-nanoalloy_MSMSE_2021
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag7Au41
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Ag7Au41
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CO_1024158701562425833262363
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AgAu-nanoalloy_MSMSE_2021
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag8Au8
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AgAu
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AB
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DFT-PBE-D3
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MD_9985399391223336507104240
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PO_7537066967786816911251827
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[
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[
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CO_1024537870578877331631840
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AgAu-nanoalloy_MSMSE_2021
|
[
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"Linfeng Zhang",
"Ben Xu",
"Han Wang"
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
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2023
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
|
10.60732/a222a0f6
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Au6
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Au
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A
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[
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CO_1024542821988784164987670
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AgAu-nanoalloy_MSMSE_2021
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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Ag29Au19
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MD_9985399391223336507104240
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[
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CO_1024664052931977021496663
|
AgAu-nanoalloy_MSMSE_2021
|
[
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"Ben Xu",
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] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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LGPL-3.0-only
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Au32
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Au
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A
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MD_9985399391223336507104240
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[
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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MD_9985399391223336507104240
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AgAu-nanoalloy_MSMSE_2021
|
[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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2023
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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MD_9985399391223336507104240
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[
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AgAu-nanoalloy_MSMSE_2021
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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Ag6Au10
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Ag3Au5
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MD_9985399391223336507104240
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CO_1024953684643450439772179
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AgAu-nanoalloy_MSMSE_2021
|
[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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2023
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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10.60732/a222a0f6
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Ag32
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Ag
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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MD_9985399391223336507104240
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CO_1025353237968178895099831
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AgAu-nanoalloy_MSMSE_2021
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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2023
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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DS_t0ec1irmeo59_0
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AgAu15
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AgAu15
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A15B
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[
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VASP
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DFT-PBE-D3
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MD_9985399391223336507104240
| 2024-08-16T15:23:42 |
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PO_4822818875781814239720558
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[
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[
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CO_1025421334851475582376030
|
AgAu-nanoalloy_MSMSE_2021
|
[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
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[
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2023
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
|
10.60732/a222a0f6
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag5Au11
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Ag5Au11
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A11B5
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[
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VASP
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DFT-PBE-D3
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MD_9985399391223336507104240
| 2024-08-16T14:13:59 |
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PO_4522791752982544156853971
| null | null | null |
[
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CO_1025466754007950407767988
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AgAu-nanoalloy_MSMSE_2021
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag3Au21
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AgAu7
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MD_9985399391223336507104240
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[
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CO_1025495476517529770338209
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AgAu-nanoalloy_MSMSE_2021
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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DS_t0ec1irmeo59_0
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Ag14Au18
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Ag7Au9
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MD_9985399391223336507104240
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CO_1025648892748337670437171
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AgAu-nanoalloy_MSMSE_2021
|
[
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] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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10.60732/a222a0f6
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag3Au13
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Ag3Au13
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A13B3
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[
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VASP
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DFT-PBE-D3
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MD_9985399391223336507104240
| 2024-08-16T14:13:23 |
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PO_8134361395816691447578150
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[
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[
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CO_1025695244770433947295579
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AgAu-nanoalloy_MSMSE_2021
|
[
"Yinan Wang",
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"Ben Xu",
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
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] | 2 | 51,766 | 51,771 | 1,188,220 | 0 | 51,766 | 0 | 51,766 | 0 | 51,766 | -68.544468 | 1,402.940203 | 0 | 2023-12-01T18:18:50 |
[
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[
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2023
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[
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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10.60732/a222a0f6
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag13Au3
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Ag13Au3
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A13B3
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[
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VASP
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DFT-PBE-D3
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MD_9985399391223336507104240
| 2024-08-16T15:31:31 |
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PO_7118130438765259237989855
| null | null | null |
[
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[
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CO_1025906184643024298114858
|
AgAu-nanoalloy_MSMSE_2021
|
[
"Yinan Wang",
"Xiaoyang Wang",
"Linfeng Zhang",
"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
"Ag",
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] | 2 | 51,766 | 51,771 | 1,188,220 | 0 | 51,766 | 0 | 51,766 | 0 | 51,766 | -68.544468 | 1,402.940203 | 0 | 2023-12-01T18:18:50 |
[
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[
3
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2023
|
[
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LGPL-3.0-only
|
{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
|
10.60732/a222a0f6
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|
DS_t0ec1irmeo59_0
|
AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
|
Ag2Au14
|
AgAu7
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A7B
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79,
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[
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[
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VASP
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DFT-PBE-D3
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[
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|
MD_9985399391223336507104240
| 2024-08-16T15:17:58 |
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PO_5059454128867099711068723
| null | null | null |
[
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] |
[
"DS_t0ec1irmeo59_0"
] | 2023-12-01T23:16:22 |
10260534166412048570396801217754891810748892973007476921879617740932853060305504809207234173623541138176452708006981609962215654542282934222013969493695903
|
CO_1026053416641204857039680
|
AgAu-nanoalloy_MSMSE_2021
|
[
"Yinan Wang",
"Xiaoyang Wang",
"Linfeng Zhang",
"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
"Ag",
"Au"
] | 2 | 51,766 | 51,771 | 1,188,220 | 0 | 51,766 | 0 | 51,766 | 0 | 51,766 | -68.544468 | 1,402.940203 | 0 | 2023-12-01T18:18:50 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.45979784888320346,
0.5402021511167966
] |
LGPL-3.0-only
|
{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
|
10.60732/a222a0f6
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag6
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Ag
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A
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DFT-PBE-D3
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MD_9985399391223336507104240
| 2024-08-16T14:13:12 |
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PO_1155222760274473685453880
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[
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CO_1026132111894449749474185
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AgAu-nanoalloy_MSMSE_2021
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
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[
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[
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2023
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[
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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10.60732/a222a0f6
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag15Au9
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Ag5Au3
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A5B3
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[
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VASP
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DFT-PBE-D3
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MD_9985399391223336507104240
| 2024-08-16T15:21:12 |
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PO_1121720849736201884748318
| null | null | null |
[
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[
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CO_1026178787466709133919737
|
AgAu-nanoalloy_MSMSE_2021
|
[
"Yinan Wang",
"Xiaoyang Wang",
"Linfeng Zhang",
"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
"Ag",
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] | 2 | 51,766 | 51,771 | 1,188,220 | 0 | 51,766 | 0 | 51,766 | 0 | 51,766 | -68.544468 | 1,402.940203 | 0 | 2023-12-01T18:18:50 |
[
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[
3
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2023
|
[
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LGPL-3.0-only
|
{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
|
10.60732/a222a0f6
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3481311511156556191035265306681170184294725465275752597341925511358567169350018029706492209753559675591025445233267163801275321275902414163540327935897054
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag15Au
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Ag15Au
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A15B
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[
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VASP
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DFT-PBE-D3
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|
MD_9985399391223336507104240
| 2024-08-16T14:26:14 |
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PO_9937011786214382159158900
| null | null | null |
[
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[
"DS_t0ec1irmeo59_0"
] | 2023-12-01T23:16:21 |
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|
CO_1026253600030110107572457
|
AgAu-nanoalloy_MSMSE_2021
|
[
"Yinan Wang",
"Xiaoyang Wang",
"Linfeng Zhang",
"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
"Ag",
"Au"
] | 2 | 51,766 | 51,771 | 1,188,220 | 0 | 51,766 | 0 | 51,766 | 0 | 51,766 | -68.544468 | 1,402.940203 | 0 | 2023-12-01T18:18:50 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.45979784888320346,
0.5402021511167966
] |
LGPL-3.0-only
|
{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
|
10.60732/a222a0f6
|
3481311511156556191035265306681170184294725465275752597341925511358567169350018029706492209753559675591025445233267163801275321275902414163540327935897054
|
DS_t0ec1irmeo59_0
|
AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
|
Ag30Au18
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Ag5Au3
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A5B3
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VASP
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DFT-PBE-D3
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{"input": {"encut": {"value": 650, "units": "eV"}}, "hash": "9985399391223336507104240349147387433243557514633906439105331955381975213318101075814762881954519768656981458682509405813528289710302917162391265942695039", "id": "MD_9985399391223336507104240"}
|
MD_9985399391223336507104240
| 2024-08-16T14:14:31 |
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|
PO_2526122432224736060142361
| null | null | null |
[
"data.809_set.000_0"
] |
[
"DS_t0ec1irmeo59_0"
] | 2023-12-01T23:16:19 |
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|
CO_1026346817902067546517655
|
AgAu-nanoalloy_MSMSE_2021
|
[
"Yinan Wang",
"Xiaoyang Wang",
"Linfeng Zhang",
"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
"Ag",
"Au"
] | 2 | 51,766 | 51,771 | 1,188,220 | 0 | 51,766 | 0 | 51,766 | 0 | 51,766 | -68.544468 | 1,402.940203 | 0 | 2023-12-01T18:18:50 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.45979784888320346,
0.5402021511167966
] |
LGPL-3.0-only
|
{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
|
10.60732/a222a0f6
|
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|
DS_t0ec1irmeo59_0
|
AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
|
Ag26Au6
|
Ag13Au3
|
A13B3
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[
"Ag",
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[
0.8125,
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| 1 |
VASP
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DFT-PBE-D3
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MD_9985399391223336507104240
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CO_1026471888940568258647786
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AgAu-nanoalloy_MSMSE_2021
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[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
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[
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2023
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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10.60732/a222a0f6
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Ag16
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DFT-PBE-D3
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MD_9985399391223336507104240
| 2024-08-16T15:32:08 |
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PO_1295270007671165079484621
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[
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CO_1026573192270604756998677
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AgAu-nanoalloy_MSMSE_2021
|
[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
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[
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2023
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[
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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10.60732/a222a0f6
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Ag5Au11
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Ag5Au11
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A11B5
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[
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DFT-PBE-D3
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MD_9985399391223336507104240
| 2024-08-16T14:13:12 |
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PO_2897192409538396549032591
| null | null | null |
[
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[
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CO_1026601155085870321793756
|
AgAu-nanoalloy_MSMSE_2021
|
[
"Yinan Wang",
"Xiaoyang Wang",
"Linfeng Zhang",
"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
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[
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] |
[
3
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2023
|
[
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
|
10.60732/a222a0f6
|
3481311511156556191035265306681170184294725465275752597341925511358567169350018029706492209753559675591025445233267163801275321275902414163540327935897054
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Au6
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Au
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A
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[
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[
1
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VASP
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DFT-PBE-D3
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MD_9985399391223336507104240
| 2024-08-16T15:07:07 |
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PO_9176285595205206357031303
| null | null | null |
[
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[
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CO_1026634695340762604607096
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AgAu-nanoalloy_MSMSE_2021
|
[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
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2023
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[
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
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10.60732/a222a0f6
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Ag6Au10
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Ag3Au5
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A5B3
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VASP
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DFT-PBE-D3
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MD_9985399391223336507104240
| 2024-08-16T14:14:35 |
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PO_7697235291907400957955139
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[
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[
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CO_1026781469717680003324880
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AgAu-nanoalloy_MSMSE_2021
|
[
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Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
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[
3
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2023
|
[
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
|
10.60732/a222a0f6
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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Au16
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Au
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A
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[
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VASP
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DFT-PBE-D3
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MD_9985399391223336507104240
| 2024-08-16T14:13:14 |
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PO_7689476722235145807048259
| null | null | null |
[
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[
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CO_1026925559904061710502515
|
AgAu-nanoalloy_MSMSE_2021
|
[
"Yinan Wang",
"Xiaoyang Wang",
"Linfeng Zhang",
"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
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] | 2 | 51,766 | 51,771 | 1,188,220 | 0 | 51,766 | 0 | 51,766 | 0 | 51,766 | -68.544468 | 1,402.940203 | 0 | 2023-12-01T18:18:50 |
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[
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2023
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[
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LGPL-3.0-only
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{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
|
10.60732/a222a0f6
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DS_t0ec1irmeo59_0
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AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
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AgAu15
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AgAu15
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A15B
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[
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VASP
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DFT-PBE-D3
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|
MD_9985399391223336507104240
| 2024-08-16T15:24:17 |
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PO_5550551056300399684594021
| null | null | null |
[
"deepmd_set.000_194"
] |
[
"DS_t0ec1irmeo59_0"
] | 2023-12-01T23:16:21 |
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CO_1026929451941086433961418
|
AgAu-nanoalloy_MSMSE_2021
|
[
"Yinan Wang",
"Xiaoyang Wang",
"Linfeng Zhang",
"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
"Ag",
"Au"
] | 2 | 51,766 | 51,771 | 1,188,220 | 0 | 51,766 | 0 | 51,766 | 0 | 51,766 | -68.544468 | 1,402.940203 | 0 | 2023-12-01T18:18:50 |
[
[
1,
1,
1
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] |
[
3
] |
2023
|
[
0.45979784888320346,
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] |
LGPL-3.0-only
|
{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
|
10.60732/a222a0f6
|
3481311511156556191035265306681170184294725465275752597341925511358567169350018029706492209753559675591025445233267163801275321275902414163540327935897054
|
DS_t0ec1irmeo59_0
|
AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
|
Au6
|
Au
|
A
|
[
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[
"Au"
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[
1
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| 1 |
VASP
|
DFT-PBE-D3
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|
MD_9985399391223336507104240
| 2024-08-16T14:29:46 |
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|
PO_6914033541642041519662331
| null | null | null |
[
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[
"DS_t0ec1irmeo59_0",
"DS_tydu7u0h0hhz_0"
] | 2023-12-01T23:25:28 |
10271418452348134992040857566065220393836286250394223444347775287522885384336448627848262563677602737816591712926847306623213253604228087067714768436615313
|
CO_1027141845234813499204085
|
AgAu-nanoalloy_MSMSE_2021
|
[
"Yinan Wang",
"Xiaoyang Wang",
"Linfeng Zhang",
"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
"Ag",
"Au"
] | 2 | 51,766 | 51,771 | 1,188,220 | 0 | 51,766 | 0 | 51,766 | 0 | 51,766 | -68.544468 | 1,402.940203 | 0 | 2023-12-01T18:18:50 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.45979784888320346,
0.5402021511167966
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LGPL-3.0-only
|
{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
|
10.60732/a222a0f6
|
3481311511156556191035265306681170184294725465275752597341925511358567169350018029706492209753559675591025445233267163801275321275902414163540327935897054
|
DS_t0ec1irmeo59_0
|
AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
|
Ag5Au11
|
Ag5Au11
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A11B5
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79,
79,
79
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[
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[
0.3125,
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[
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[
0,
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[
0,
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true
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VASP
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DFT-PBE-D3
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[
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] | true | null | null | -45.079301 | null | 0.053626 | 0.148701 |
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|
MD_9985399391223336507104240
| 2024-08-16T15:23:22 |
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PO_1020568282105362605933485
| null | null | null |
[
"deepmd_set.000_187"
] |
[
"DS_t0ec1irmeo59_0"
] | 2023-12-01T23:16:22 |
10272532025339085428417510124921255606115631716311734470779040126983837797997181008066955158362113249174963369263094761522599839483355959160259696928610481
|
CO_1027253202533908542841751
|
AgAu-nanoalloy_MSMSE_2021
|
[
"Yinan Wang",
"Xiaoyang Wang",
"Linfeng Zhang",
"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
"Ag",
"Au"
] | 2 | 51,766 | 51,771 | 1,188,220 | 0 | 51,766 | 0 | 51,766 | 0 | 51,766 | -68.544468 | 1,402.940203 | 0 | 2023-12-01T18:18:50 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.45979784888320346,
0.5402021511167966
] |
LGPL-3.0-only
|
{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
|
10.60732/a222a0f6
|
3481311511156556191035265306681170184294725465275752597341925511358567169350018029706492209753559675591025445233267163801275321275902414163540327935897054
|
DS_t0ec1irmeo59_0
|
AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
|
Ag13Au3
|
Ag13Au3
|
A13B3
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47,
47,
47,
47,
47,
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47,
47,
47,
79,
79,
79
] |
[
"Ag",
"Au"
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[
0.8125,
0.1875
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[
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[
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0
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[
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[
true,
true,
true
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[
1,
1,
1
] | 3 |
10231033413871723162521799902596231757300767388287112467443144471953674496591732026911196115308930418953990784663434045697107487093770924031021395629285888
| 1 |
VASP
|
DFT-PBE-D3
| null |
[
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[
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] | null |
[
[
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[
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[
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] | true | null | null | -42.250584 | null | 3.779999 | 8.807576 |
{"input": {"encut": {"value": 650, "units": "eV"}}, "hash": "9985399391223336507104240349147387433243557514633906439105331955381975213318101075814762881954519768656981458682509405813528289710302917162391265942695039", "id": "MD_9985399391223336507104240"}
|
MD_9985399391223336507104240
| 2024-08-16T15:21:16 |
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|
PO_1262162521962900796571448
| null | null | null |
[
"data.464_set.000_2"
] |
[
"DS_t0ec1irmeo59_0"
] | 2023-12-01T23:16:19 |
10272554194307751017909178428411196710003810160732233386066043885540006451102076874511012190946423368764014570218850043953444472198093539314507479193787795
|
CO_1027255419430775101790917
|
AgAu-nanoalloy_MSMSE_2021
|
[
"Yinan Wang",
"Xiaoyang Wang",
"Linfeng Zhang",
"Ben Xu",
"Han Wang"
] |
Approximately 50,000 configurations of Au, Ag and AuAg used as part of a training dataset for a DP-GEN-based ML model for a Ag-Au nanoalloy potential.
|
[
"Ag",
"Au"
] | 2 | 51,766 | 51,771 | 1,188,220 | 0 | 51,766 | 0 | 51,766 | 0 | 51,766 | -68.544468 | 1,402.940203 | 0 | 2023-12-01T18:18:50 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.45979784888320346,
0.5402021511167966
] |
LGPL-3.0-only
|
{'source-publication': 'https://doi.org/10.48550/arXiv.2108.06232', 'source-data': 'https://www.aissquare.com/datasets/detail?pageType=datasets&name=AgAu-nanoalloy', 'other': None}
|
10.60732/a222a0f6
|
3481311511156556191035265306681170184294725465275752597341925511358567169350018029706492209753559675591025445233267163801275321275902414163540327935897054
|
DS_t0ec1irmeo59_0
|
AgAu-nanoalloy_MSMSE_2021__Wang-Wang-Zhang-Xu-Wang__DS_t0ec1irmeo59_0
|
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