Dataset Viewer
chemical_formula_hill
string | chemical_formula_reduced
string | chemical_formula_anonymous
string | atomic_numbers
sequence | elements
sequence | elements_ratios
sequence | nelements
int32 | nsites
int32 | cell
sequence | positions
sequence | pbc
sequence | dimension_types
sequence | nperiodic_dimensions
int32 | structure_hash
string | multiplicity
int32 | software
string | method
string | adsorption_energy
float64 | atomic_forces
sequence | atomization_energy
float64 | cauchy_stress
sequence | 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
sequence | configuration_names
sequence | configuration_dataset_ids
sequence | configuration_last_modified
timestamp[ns] | configuration_hash
string | configuration_id
string | dataset_name
string | dataset_authors
sequence | dataset_description
string | dataset_elements
sequence | 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
sequence | dataset_nperiodic_dimensions
sequence | dataset_publication_year
string | dataset_total_elements_ratios
sequence | dataset_license
string | dataset_links
string | dataset_doi
string | dataset_hash
string | dataset_id
string | dataset_extended_id
string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
C5Cu192O6
|
C5Cu192O6
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A192B6C5
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{"input": {"cutoff-energy": {"value": 300, "units": "Ry"}, "relative-cutoff-energy": {"value": 60, "units": "Ry"}, "scheme": "QUICKSTEP"}, "hash": "4178802015180711158523341789665647106270915003175636797223149523631617352424712633254110378226507537845096425680076758458702686284378534857806338422045244", "id": "MD_4178802015180711158523341"}
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MD_4178802015180711158523341
| 2024-08-16T14:36:26 |
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PO_2581145304994484098101930
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MD_1404919777827893392269834
| null |
[
"C_G_M_-_M_L_P_-_C_u_-_C_-_O_Training_Dataset_Cu-C-O__default_158"
] |
[
"DS_xho213jy5pf9_0"
] | 2024-01-12T22:57:47 |
10007853562795269016369557451605987909076331801674590978484160703241519515127519142734219512160753480047571687532261480249999665699321014410550735894092066
|
CO_1000785356279526901636955
|
CGM-MLP_natcomm2023_Cu-C-O
|
[
"Di Zhang",
"Peiyun Yi",
"Xinmin Lai",
"Linfa Peng",
"Hao Li"
] |
Training simulations from CGM-MLP_natcomm2023 of carbon on an oxygen-contaminated Cu surface. This dataset was one of the datasets used in training during the process of producing an active learning dataset for the purposes of exploring substrate-catalyzed deposition on metal surfaces such as Cu(111), Cr(110), Ti(001), and oxygen-contaminated Cu(111) as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu, Cr and Ti surfaces.
|
[
"C",
"Cu",
"O"
] | 3 | 1,717 | 1,717 | 387,151 | 0 | 1,717 | 0 | 1,717 | 0 | 1,717 | -869.912036 | 16,129.776508 | 0 | 2024-01-12T18:00:23 |
[
[
1,
1,
1
]
] |
[
3
] |
2024
|
[
0.12187751032542858,
0.8515127172601905,
0.026609772414380953
] |
MIT
|
{'source-publication': 'https://doi.org/10.1038/s41467-023-44525-z', 'source-data': 'https://github.com/sjtudizhang/CGM-MLP', 'other': None}
|
10.60732/215303a5
|
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|
DS_xho213jy5pf9_0
|
CGM-MLP_natcomm2023_Cu-C-O__Zhang-Yi-Lai-Peng-Li__DS_xho213jy5pf9_0
|
C8Cu192O6
|
C4Cu96O3
|
A96B4C3
|
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MD_4178802015180711158523341
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MD_1404919777827893392269834
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[
"C_G_M_-_M_L_P_-_C_u_-_C_-_O_Training_Dataset_Cu-C-O__default_95"
] |
[
"DS_xho213jy5pf9_0"
] | 2024-01-12T22:57:47 |
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|
CO_1001155241301240706097522
|
CGM-MLP_natcomm2023_Cu-C-O
|
[
"Di Zhang",
"Peiyun Yi",
"Xinmin Lai",
"Linfa Peng",
"Hao Li"
] |
Training simulations from CGM-MLP_natcomm2023 of carbon on an oxygen-contaminated Cu surface. This dataset was one of the datasets used in training during the process of producing an active learning dataset for the purposes of exploring substrate-catalyzed deposition on metal surfaces such as Cu(111), Cr(110), Ti(001), and oxygen-contaminated Cu(111) as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu, Cr and Ti surfaces.
|
[
"C",
"Cu",
"O"
] | 3 | 1,717 | 1,717 | 387,151 | 0 | 1,717 | 0 | 1,717 | 0 | 1,717 | -869.912036 | 16,129.776508 | 0 | 2024-01-12T18:00:23 |
[
[
1,
1,
1
]
] |
[
3
] |
2024
|
[
0.12187751032542858,
0.8515127172601905,
0.026609772414380953
] |
MIT
|
{'source-publication': 'https://doi.org/10.1038/s41467-023-44525-z', 'source-data': 'https://github.com/sjtudizhang/CGM-MLP', 'other': None}
|
10.60732/215303a5
|
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|
DS_xho213jy5pf9_0
|
CGM-MLP_natcomm2023_Cu-C-O__Zhang-Yi-Lai-Peng-Li__DS_xho213jy5pf9_0
|
C25Cu192O6
|
C25Cu192O6
|
A192B25C6
|
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MD_4178802015180711158523341
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MD_1404919777827893392269834
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[
"C_G_M_-_M_L_P_-_C_u_-_C_-_O_Training_Dataset_Cu-C-O__default_630"
] |
[
"DS_xho213jy5pf9_0"
] | 2024-01-12T22:57:49 |
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|
CO_1005195331959183359580936
|
CGM-MLP_natcomm2023_Cu-C-O
|
[
"Di Zhang",
"Peiyun Yi",
"Xinmin Lai",
"Linfa Peng",
"Hao Li"
] |
Training simulations from CGM-MLP_natcomm2023 of carbon on an oxygen-contaminated Cu surface. This dataset was one of the datasets used in training during the process of producing an active learning dataset for the purposes of exploring substrate-catalyzed deposition on metal surfaces such as Cu(111), Cr(110), Ti(001), and oxygen-contaminated Cu(111) as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu, Cr and Ti surfaces.
|
[
"C",
"Cu",
"O"
] | 3 | 1,717 | 1,717 | 387,151 | 0 | 1,717 | 0 | 1,717 | 0 | 1,717 | -869.912036 | 16,129.776508 | 0 | 2024-01-12T18:00:23 |
[
[
1,
1,
1
]
] |
[
3
] |
2024
|
[
0.12187751032542858,
0.8515127172601905,
0.026609772414380953
] |
MIT
|
{'source-publication': 'https://doi.org/10.1038/s41467-023-44525-z', 'source-data': 'https://github.com/sjtudizhang/CGM-MLP', 'other': None}
|
10.60732/215303a5
|
2674213658108886156269833982745617215408125360365717223487190489887377227881369070388462348415461944816330887697544112489580763300291995767696795375582937
|
DS_xho213jy5pf9_0
|
CGM-MLP_natcomm2023_Cu-C-O__Zhang-Yi-Lai-Peng-Li__DS_xho213jy5pf9_0
|
C39Cu192O6
|
C13Cu64O2
|
A64B13C2
|
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[
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[
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] | 2024-01-12T22:57:48 |
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CO_1012341686090401106928493
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CGM-MLP_natcomm2023_Cu-C-O
|
[
"Di Zhang",
"Peiyun Yi",
"Xinmin Lai",
"Linfa Peng",
"Hao Li"
] |
Training simulations from CGM-MLP_natcomm2023 of carbon on an oxygen-contaminated Cu surface. This dataset was one of the datasets used in training during the process of producing an active learning dataset for the purposes of exploring substrate-catalyzed deposition on metal surfaces such as Cu(111), Cr(110), Ti(001), and oxygen-contaminated Cu(111) as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu, Cr and Ti surfaces.
|
[
"C",
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[
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[
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2024
|
[
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MIT
|
{'source-publication': 'https://doi.org/10.1038/s41467-023-44525-z', 'source-data': 'https://github.com/sjtudizhang/CGM-MLP', 'other': None}
|
10.60732/215303a5
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|
DS_xho213jy5pf9_0
|
CGM-MLP_natcomm2023_Cu-C-O__Zhang-Yi-Lai-Peng-Li__DS_xho213jy5pf9_0
|
C3Cu192O6
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CCu64O2
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A64B2C
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{"input": {"cutoff-energy": {"value": 300, "units": "Ry"}, "relative-cutoff-energy": {"value": 60, "units": "Ry"}, "scheme": "QUICKSTEP"}, "hash": "4178802015180711158523341789665647106270915003175636797223149523631617352424712633254110378226507537845096425680076758458702686284378534857806338422045244", "id": "MD_4178802015180711158523341"}
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MD_4178802015180711158523341
| 2024-08-16T14:58:41 |
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PO_1238294832805875174085928
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MD_1404919777827893392269834
| null |
[
"C_G_M_-_M_L_P_-_C_u_-_C_-_O_Training_Dataset_Cu-C-O__default_12"
] |
[
"DS_xho213jy5pf9_0"
] | 2024-01-12T22:57:46 |
10213228609009446192367260400197729683064322383842145670969756821667151768359875381168961144428164166017399585952476573148981144290958158251917253331996146
|
CO_1021322860900944619236726
|
CGM-MLP_natcomm2023_Cu-C-O
|
[
"Di Zhang",
"Peiyun Yi",
"Xinmin Lai",
"Linfa Peng",
"Hao Li"
] |
Training simulations from CGM-MLP_natcomm2023 of carbon on an oxygen-contaminated Cu surface. This dataset was one of the datasets used in training during the process of producing an active learning dataset for the purposes of exploring substrate-catalyzed deposition on metal surfaces such as Cu(111), Cr(110), Ti(001), and oxygen-contaminated Cu(111) as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu, Cr and Ti surfaces.
|
[
"C",
"Cu",
"O"
] | 3 | 1,717 | 1,717 | 387,151 | 0 | 1,717 | 0 | 1,717 | 0 | 1,717 | -869.912036 | 16,129.776508 | 0 | 2024-01-12T18:00:23 |
[
[
1,
1,
1
]
] |
[
3
] |
2024
|
[
0.12187751032542858,
0.8515127172601905,
0.026609772414380953
] |
MIT
|
{'source-publication': 'https://doi.org/10.1038/s41467-023-44525-z', 'source-data': 'https://github.com/sjtudizhang/CGM-MLP', 'other': None}
|
10.60732/215303a5
|
2674213658108886156269833982745617215408125360365717223487190489887377227881369070388462348415461944816330887697544112489580763300291995767696795375582937
|
DS_xho213jy5pf9_0
|
CGM-MLP_natcomm2023_Cu-C-O__Zhang-Yi-Lai-Peng-Li__DS_xho213jy5pf9_0
|
C44Cu192O6
|
C22Cu96O3
|
A96B22C3
|
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MD_4178802015180711158523341
| 2024-08-16T14:32:11 |
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MD_1404919777827893392269834
| null |
[
"C_G_M_-_M_L_P_-_C_u_-_C_-_O_Training_Dataset_Cu-C-O__default_1688"
] |
[
"DS_xho213jy5pf9_0"
] | 2024-01-12T22:57:50 |
10270099968689156140846975135497091653127916690158406218315114919936100670383166446464598696726537448114989311368362494343021817320923437053662707920872269
|
CO_1027009996868915614084697
|
CGM-MLP_natcomm2023_Cu-C-O
|
[
"Di Zhang",
"Peiyun Yi",
"Xinmin Lai",
"Linfa Peng",
"Hao Li"
] |
Training simulations from CGM-MLP_natcomm2023 of carbon on an oxygen-contaminated Cu surface. This dataset was one of the datasets used in training during the process of producing an active learning dataset for the purposes of exploring substrate-catalyzed deposition on metal surfaces such as Cu(111), Cr(110), Ti(001), and oxygen-contaminated Cu(111) as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu, Cr and Ti surfaces.
|
[
"C",
"Cu",
"O"
] | 3 | 1,717 | 1,717 | 387,151 | 0 | 1,717 | 0 | 1,717 | 0 | 1,717 | -869.912036 | 16,129.776508 | 0 | 2024-01-12T18:00:23 |
[
[
1,
1,
1
]
] |
[
3
] |
2024
|
[
0.12187751032542858,
0.8515127172601905,
0.026609772414380953
] |
MIT
|
{'source-publication': 'https://doi.org/10.1038/s41467-023-44525-z', 'source-data': 'https://github.com/sjtudizhang/CGM-MLP', 'other': None}
|
10.60732/215303a5
|
2674213658108886156269833982745617215408125360365717223487190489887377227881369070388462348415461944816330887697544112489580763300291995767696795375582937
|
DS_xho213jy5pf9_0
|
CGM-MLP_natcomm2023_Cu-C-O__Zhang-Yi-Lai-Peng-Li__DS_xho213jy5pf9_0
|
C22Cu192O6
|
C11Cu96O3
|
A96B11C3
|
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MD_4178802015180711158523341
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MD_1404919777827893392269834
| null |
[
"C_G_M_-_M_L_P_-_C_u_-_C_-_O_Training_Dataset_Cu-C-O__default_520"
] |
[
"DS_xho213jy5pf9_0"
] | 2024-01-12T22:57:49 |
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|
CO_1055654834443681824190302
|
CGM-MLP_natcomm2023_Cu-C-O
|
[
"Di Zhang",
"Peiyun Yi",
"Xinmin Lai",
"Linfa Peng",
"Hao Li"
] |
Training simulations from CGM-MLP_natcomm2023 of carbon on an oxygen-contaminated Cu surface. This dataset was one of the datasets used in training during the process of producing an active learning dataset for the purposes of exploring substrate-catalyzed deposition on metal surfaces such as Cu(111), Cr(110), Ti(001), and oxygen-contaminated Cu(111) as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu, Cr and Ti surfaces.
|
[
"C",
"Cu",
"O"
] | 3 | 1,717 | 1,717 | 387,151 | 0 | 1,717 | 0 | 1,717 | 0 | 1,717 | -869.912036 | 16,129.776508 | 0 | 2024-01-12T18:00:23 |
[
[
1,
1,
1
]
] |
[
3
] |
2024
|
[
0.12187751032542858,
0.8515127172601905,
0.026609772414380953
] |
MIT
|
{'source-publication': 'https://doi.org/10.1038/s41467-023-44525-z', 'source-data': 'https://github.com/sjtudizhang/CGM-MLP', 'other': None}
|
10.60732/215303a5
|
2674213658108886156269833982745617215408125360365717223487190489887377227881369070388462348415461944816330887697544112489580763300291995767696795375582937
|
DS_xho213jy5pf9_0
|
CGM-MLP_natcomm2023_Cu-C-O__Zhang-Yi-Lai-Peng-Li__DS_xho213jy5pf9_0
|
C2Cu192O6
|
CCu96O3
|
A96B3C
|
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MD_4178802015180711158523341
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[
"C_G_M_-_M_L_P_-_C_u_-_C_-_O_Training_Dataset_Cu-C-O__default_146"
] |
[
"DS_xho213jy5pf9_0"
] | 2024-01-12T22:57:47 |
10578345642506184614686506351929085441076061369694059647404721185939006785717320342132824808119854087445622869437049227081266852415555681629093649157220148
|
CO_1057834564250618461468650
|
CGM-MLP_natcomm2023_Cu-C-O
|
[
"Di Zhang",
"Peiyun Yi",
"Xinmin Lai",
"Linfa Peng",
"Hao Li"
] |
Training simulations from CGM-MLP_natcomm2023 of carbon on an oxygen-contaminated Cu surface. This dataset was one of the datasets used in training during the process of producing an active learning dataset for the purposes of exploring substrate-catalyzed deposition on metal surfaces such as Cu(111), Cr(110), Ti(001), and oxygen-contaminated Cu(111) as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu, Cr and Ti surfaces.
|
[
"C",
"Cu",
"O"
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[
[
1,
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1
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] |
[
3
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2024
|
[
0.12187751032542858,
0.8515127172601905,
0.026609772414380953
] |
MIT
|
{'source-publication': 'https://doi.org/10.1038/s41467-023-44525-z', 'source-data': 'https://github.com/sjtudizhang/CGM-MLP', 'other': None}
|
10.60732/215303a5
|
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|
DS_xho213jy5pf9_0
|
CGM-MLP_natcomm2023_Cu-C-O__Zhang-Yi-Lai-Peng-Li__DS_xho213jy5pf9_0
|
C26Cu192O6
|
C13Cu96O3
|
A96B13C3
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{"input": {"cutoff-energy": {"value": 300, "units": "Ry"}, "relative-cutoff-energy": {"value": 60, "units": "Ry"}, "scheme": "QUICKSTEP"}, "hash": "4178802015180711158523341789665647106270915003175636797223149523631617352424712633254110378226507537845096425680076758458702686284378534857806338422045244", "id": "MD_4178802015180711158523341"}
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MD_4178802015180711158523341
| 2024-08-16T14:31:54 |
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PO_1052581679222156066489170
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MD_1404919777827893392269834
| null |
[
"C_G_M_-_M_L_P_-_C_u_-_C_-_O_Training_Dataset_Cu-C-O__default_677"
] |
[
"DS_xho213jy5pf9_0"
] | 2024-01-12T22:57:49 |
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|
CO_1063502716069112956313502
|
CGM-MLP_natcomm2023_Cu-C-O
|
[
"Di Zhang",
"Peiyun Yi",
"Xinmin Lai",
"Linfa Peng",
"Hao Li"
] |
Training simulations from CGM-MLP_natcomm2023 of carbon on an oxygen-contaminated Cu surface. This dataset was one of the datasets used in training during the process of producing an active learning dataset for the purposes of exploring substrate-catalyzed deposition on metal surfaces such as Cu(111), Cr(110), Ti(001), and oxygen-contaminated Cu(111) as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu, Cr and Ti surfaces.
|
[
"C",
"Cu",
"O"
] | 3 | 1,717 | 1,717 | 387,151 | 0 | 1,717 | 0 | 1,717 | 0 | 1,717 | -869.912036 | 16,129.776508 | 0 | 2024-01-12T18:00:23 |
[
[
1,
1,
1
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] |
[
3
] |
2024
|
[
0.12187751032542858,
0.8515127172601905,
0.026609772414380953
] |
MIT
|
{'source-publication': 'https://doi.org/10.1038/s41467-023-44525-z', 'source-data': 'https://github.com/sjtudizhang/CGM-MLP', 'other': None}
|
10.60732/215303a5
|
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|
DS_xho213jy5pf9_0
|
CGM-MLP_natcomm2023_Cu-C-O__Zhang-Yi-Lai-Peng-Li__DS_xho213jy5pf9_0
|
C14Cu192O6
|
C7Cu96O3
|
A96B7C3
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{"input": {"cutoff-energy": {"value": 300, "units": "Ry"}, "relative-cutoff-energy": {"value": 60, "units": "Ry"}, "scheme": "QUICKSTEP"}, "hash": "4178802015180711158523341789665647106270915003175636797223149523631617352424712633254110378226507537845096425680076758458702686284378534857806338422045244", "id": "MD_4178802015180711158523341"}
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MD_4178802015180711158523341
| 2024-08-16T15:25:53 |
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PO_8756793173806955905822764
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|
MD_1404919777827893392269834
| null |
[
"C_G_M_-_M_L_P_-_C_u_-_C_-_O_Training_Dataset_Cu-C-O__default_401"
] |
[
"DS_xho213jy5pf9_0"
] | 2024-01-12T22:57:48 |
10715505137940027291759998485814690077552057093943737143447223727904003224981603146499545469264721581149554605050368445936145650652914012671547866776471872
|
CO_1071550513794002729175999
|
CGM-MLP_natcomm2023_Cu-C-O
|
[
"Di Zhang",
"Peiyun Yi",
"Xinmin Lai",
"Linfa Peng",
"Hao Li"
] |
Training simulations from CGM-MLP_natcomm2023 of carbon on an oxygen-contaminated Cu surface. This dataset was one of the datasets used in training during the process of producing an active learning dataset for the purposes of exploring substrate-catalyzed deposition on metal surfaces such as Cu(111), Cr(110), Ti(001), and oxygen-contaminated Cu(111) as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu, Cr and Ti surfaces.
|
[
"C",
"Cu",
"O"
] | 3 | 1,717 | 1,717 | 387,151 | 0 | 1,717 | 0 | 1,717 | 0 | 1,717 | -869.912036 | 16,129.776508 | 0 | 2024-01-12T18:00:23 |
[
[
1,
1,
1
]
] |
[
3
] |
2024
|
[
0.12187751032542858,
0.8515127172601905,
0.026609772414380953
] |
MIT
|
{'source-publication': 'https://doi.org/10.1038/s41467-023-44525-z', 'source-data': 'https://github.com/sjtudizhang/CGM-MLP', 'other': None}
|
10.60732/215303a5
|
2674213658108886156269833982745617215408125360365717223487190489887377227881369070388462348415461944816330887697544112489580763300291995767696795375582937
|
DS_xho213jy5pf9_0
|
CGM-MLP_natcomm2023_Cu-C-O__Zhang-Yi-Lai-Peng-Li__DS_xho213jy5pf9_0
|
C33Cu192O6
|
C11Cu64O2
|
A64B11C2
|
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] | true | null | null | -910.108278 | null | 2.161266 | 21.877652 |
{"input": {"cutoff-energy": {"value": 300, "units": "Ry"}, "relative-cutoff-energy": {"value": 60, "units": "Ry"}, "scheme": "QUICKSTEP"}, "hash": "4178802015180711158523341789665647106270915003175636797223149523631617352424712633254110378226507537845096425680076758458702686284378534857806338422045244", "id": "MD_4178802015180711158523341"}
|
MD_4178802015180711158523341
| 2024-08-16T14:12:25 |
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PO_3450308484056335009766026
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{"config_type": "default", "hash": "1404919777827893392269834943670411845301261407866846710153021606405314199411070807160658984221877463606755954368807568728599557946099287677348404211930695", "id": "MD_1404919777827893392269834"}
|
MD_1404919777827893392269834
| null |
[
"C_G_M_-_M_L_P_-_C_u_-_C_-_O_Training_Dataset_Cu-C-O__default_1111"
] |
[
"DS_xho213jy5pf9_0"
] | 2024-01-12T22:57:48 |
10836499086048525512550153570211187545409865219786827461125114516879956003609895674947784235379646322486956821644361963130472229742242839653534114375628283
|
CO_1083649908604852551255015
|
CGM-MLP_natcomm2023_Cu-C-O
|
[
"Di Zhang",
"Peiyun Yi",
"Xinmin Lai",
"Linfa Peng",
"Hao Li"
] |
Training simulations from CGM-MLP_natcomm2023 of carbon on an oxygen-contaminated Cu surface. This dataset was one of the datasets used in training during the process of producing an active learning dataset for the purposes of exploring substrate-catalyzed deposition on metal surfaces such as Cu(111), Cr(110), Ti(001), and oxygen-contaminated Cu(111) as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu, Cr and Ti surfaces.
|
[
"C",
"Cu",
"O"
] | 3 | 1,717 | 1,717 | 387,151 | 0 | 1,717 | 0 | 1,717 | 0 | 1,717 | -869.912036 | 16,129.776508 | 0 | 2024-01-12T18:00:23 |
[
[
1,
1,
1
]
] |
[
3
] |
2024
|
[
0.12187751032542858,
0.8515127172601905,
0.026609772414380953
] |
MIT
|
{'source-publication': 'https://doi.org/10.1038/s41467-023-44525-z', 'source-data': 'https://github.com/sjtudizhang/CGM-MLP', 'other': None}
|
10.60732/215303a5
|
2674213658108886156269833982745617215408125360365717223487190489887377227881369070388462348415461944816330887697544112489580763300291995767696795375582937
|
DS_xho213jy5pf9_0
|
CGM-MLP_natcomm2023_Cu-C-O__Zhang-Yi-Lai-Peng-Li__DS_xho213jy5pf9_0
|
End of preview. Expand
in Data Studio
Dataset
CGM-MLP natcomm2023 Cu-C-O
Description
Training simulations from CGM-MLP_natcomm2023 of carbon on an oxygen-contaminated Cu surface. This dataset was one of the datasets used in training during the process of producing an active learning dataset for the purposes of exploring substrate-catalyzed deposition on metal surfaces such as Cu(111), Cr(110), Ti(001), and oxygen-contaminated Cu(111) as a means of controllable synthesis of carbon nanomaterials. The combined dataset includes structures from the Carbon_GAP_20 dataset and additional configurations of carbon clusters on a Cu, Cr and Ti surfaces.
Additional details stored in dataset columns prepended with "dataset_".
Dataset authors
Di Zhang, Peiyun Yi, Xinmin Lai, Linfa Peng, Hao Li
Publication
https://doi.org/10.1038/s41467-023-44525-z
Original data link
https://github.com/sjtudizhang/CGM-MLP
License
MIT
Number of unique molecular configurations
1717
Number of atoms
387151
Elements included
C, Cu, O
Properties included
energy, atomic forces, cauchy stress
Cite this dataset
Zhang, D., Yi, P., Lai, X., Peng, L., and Li, H. CGM-MLP natcomm2023 Cu-C-O. ColabFit, 2024. https://doi.org/10.60732/215303a5
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