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| 40 graphite structures with different lattice constants ranging from 2.0 to 3.2 Å, with a 0.03 Å increment. This dataset was one of the datasets used in testing screening parameters during the process of producing an active learning dataset for Cu-C interactions for the purposes of exploring substrate-catalyzed deposition 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(111) surface. | [
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| null | [
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| true | null | null | -429.049321 | null | 0.768919 | 1.058606 | {"input": {"cutoff-energy": {"value": 300, "units": "Ry"}, "relative-cutoff-energy": {"value": 60, "units": "Ry"}, "scheme": "QUICKSTEP"}, "hash": "4178802015180711158523341789665647106270915003175636797223149523631617352424712633254110378226507537845096425680076758458702686284378534857806338422045244", "id": "MD_4178802015180711158523341"} | MD_4178802015180711158523341 | 2024-08-16T15:26:09 | 2173327871556024156344237298561031663230090552151883644844124490972579652491838729417653294043218743128473119658743295300693853976536881088237156099391186 | PO_2173327871556024156344237 | {"config_type": "deposition", "hash": "5085563091843178930982360100539406021215696347708504656720957170424303591863714685556596531403026063781709596599516565635016083334750335589969203795829992", "id": "MD_5085563091843178930982360"} | MD_5085563091843178930982360 | null | [
"Graphite_Training_Set_9"
]
| [
"DS_jasbxoigo7r4_0"
]
| 2024-01-11T22:51:36 | 3979676348736446767105450363739554243708029313537914671053440746383038848310993064790729043999951313017223034975236565117915647508118405075333887545915221 | CO_3979676348736446767105450 | CGM-MLP_natcomm2023_screening_graphite_train | [
"Di Zhang",
"Peiyun Yi",
"Xinmin Lai",
"Linfa Peng",
"Hao Li"
]
| 40 graphite structures with different lattice constants ranging from 2.0 to 3.2 Å, with a 0.03 Å increment. This dataset was one of the datasets used in testing screening parameters during the process of producing an active learning dataset for Cu-C interactions for the purposes of exploring substrate-catalyzed deposition 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(111) surface. | [
"C"
]
| 1 | 41 | 41 | 1,968 | 0 | 41 | 0 | 41 | 0 | 41 | -421.657702 | 2,282.01638 | 0 | 2024-01-11T17:51:44 | [
[
1,
1,
1
]
]
| [
3
]
| 2024 | [
1
]
| MIT | {'source-publication': 'https://doi.org/10.1038/s41467-023-44525-z', 'source-data': 'https://github.com/sjtudizhang/CGM-MLP', 'other': None} | 10.60732/85590078 | 2494588041141673332406206292251473023331313652700847532472270691392699352857943825066661224195639747022221580900993038658514516850659320762998185331662087 | DS_jasbxoigo7r4_0 | CGM-MLP_natcomm2023_screening_graphite_train__Zhang-Yi-Lai-Peng-Li__DS_jasbxoigo7r4_0 |
Dataset
CGM-MLP natcomm2023 screening graphite train
Description
40 graphite structures with different lattice constants ranging from 2.0 to 3.2 Å, with a 0.03 Å increment. This dataset was one of the datasets used in testing screening parameters during the process of producing an active learning dataset for Cu-C interactions for the purposes of exploring substrate-catalyzed deposition 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(111) surface.
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
41
Number of atoms
1968
Elements included
C
Properties included
energy, atomic forces, cauchy stress
Cite this dataset
Zhang, D., Yi, P., Lai, X., Peng, L., and Li, H. CGM-MLP natcomm2023 screening graphite train. ColabFit, 2024. https://doi.org/10.60732/85590078
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