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 |
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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"}
|
MD_4178802015180711158523341
| 2024-08-16T15:17:15 |
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MD_9321007856532242169222229
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[
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[
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CO_1000250624900832020493697
|
CGM-MLP_natcomm2023_GAP_20
|
[
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"Peiyun Yi",
"Xinmin Lai",
"Linfa Peng",
"Hao Li"
] |
Carbon_GAP_20 dataset from CGM-MLP_natcomm2023. 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.
|
[
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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/b996b7e0
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DS_qslbmhe4bcgg_0
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CGM-MLP_natcomm2023_GAP_20__Zhang-Yi-Lai-Peng-Li__DS_qslbmhe4bcgg_0
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[
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Carbon_GAP_20 dataset from CGM-MLP_natcomm2023. 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.
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[
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Carbon_GAP_20 dataset from CGM-MLP_natcomm2023. 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.
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[
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Carbon_GAP_20 dataset from CGM-MLP_natcomm2023. 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.
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[
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Carbon_GAP_20 dataset from CGM-MLP_natcomm2023. 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.
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{'source-publication': 'https://doi.org/10.1038/s41467-023-44525-z', 'source-data': 'https://github.com/sjtudizhang/CGM-MLP', 'other': None}
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C170
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|
MD_4178802015180711158523341
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PO_9042997246997155041668589
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MD_1207782181610877799959501
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[
"Defects",
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] |
[
"DS_on6fusdw5n8q_0",
"DS_sz9me9r61581_0",
"DS_qslbmhe4bcgg_0"
] | 2024-01-12T23:02:03 |
10324937225082910221402873778689186765837825831161601525291353091443243311643089558957543870793281527548892204300998495442233852980799410686859652605839314
|
CO_1032493722508291022140287
|
CGM-MLP_natcomm2023_GAP_20
|
[
"Di Zhang",
"Peiyun Yi",
"Xinmin Lai",
"Linfa Peng",
"Hao Li"
] |
Carbon_GAP_20 dataset from CGM-MLP_natcomm2023. 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"
] | 2 | 6,178 | 6,178 | 400,485 | 0 | 6,178 | 0 | 6,178 | 0 | 6,178 | -610.674193 | 322,139.879783 | 0 | 2024-01-12T18:05:32 |
[
[
1,
1,
1
]
] |
[
3
] |
2024
|
[
0.9997003633094872,
0.0002996366905127533
] |
MIT
|
{'source-publication': 'https://doi.org/10.1038/s41467-023-44525-z', 'source-data': 'https://github.com/sjtudizhang/CGM-MLP', 'other': None}
|
10.60732/b996b7e0
|
13080048435808369926616504673712039355594112016670053401056446991629712581860863129988650936563782732383834735283550013873920729429116510018161075938492919
|
DS_qslbmhe4bcgg_0
|
CGM-MLP_natcomm2023_GAP_20__Zhang-Yi-Lai-Peng-Li__DS_qslbmhe4bcgg_0
|
End of preview. Expand
in Data Studio
Dataset
CGM-MLP natcomm2023 GAP 20
Description
Carbon_GAP_20 dataset from CGM-MLP_natcomm2023. 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
6178
Number of atoms
400485
Elements included
C, Cu
Properties included
energy, atomic forces, cauchy stress
Cite this dataset
Zhang, D., Yi, P., Lai, X., Peng, L., and Li, H. CGM-MLP natcomm2023 GAP 20. ColabFit, 2024. https://doi.org/10.60732/b996b7e0
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