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2025-04-17T18:48:22
DS_taxwl7jo4f8a_0
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2025-04-17T18:26:41
DS_taxwl7jo4f8a_0
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2025-04-17T17:35:57
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2025-04-16T15:18:28
DS_taxwl7jo4f8a_0
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2025-04-16T18:30:32
DS_taxwl7jo4f8a_0
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2025-04-16T17:26:47
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2025-04-17T18:37:27
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11422432904156167825921000939160555654545473011162235574459525363671460969157123004093199257164647945278693884685137453454525674221256425413414815177382723
[ [ 7.252793, 0, 0 ], [ 0, 7.252793, 0 ], [ 0, 0, 7.252793 ] ]
[ [ 0.36163151, 0.50596935, 3.74141854 ], [ 0.63801369, 0.18412666, 1.10647159 ], [ 3.5404509, 4.73993231, 6.26561534 ], [ 3.58922594, 1.89680845, 6.63935177 ], [ 3.72705076, 4.33656823, 0.47051044 ], [ 2.56437002, 4.58430188, ...
[ true, true, true ]
C64
C
A
[ "C" ]
[ 1 ]
[ 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6...
64
1
3
[ 1, 1, 1 ]
[ "Amorphous_Bulk", "Amorphous_Carbon_Training_Set_1642", "G_A_P_-_2_0_Training_Dataset_Amorphous_Bulk__Amorphous_Bulk_1958" ]
null
data/MD/3341/MD_4178802015180711158523341.json
data/MD/2229/MD_9321007856532242169222229.json
null
{"config_type": "Amorphous_Bulk", "hash": "9321007856532242169222229034018050775805146400287982340713513919047442689138559910921405913858210882301955649016535905049146021089235359446976918785169490", "id": "MD_9321007856532242169222229"}
{"input": {"cutoff-energy": {"value": 300, "units": "Ry"}, "relative-cutoff-energy": {"value": 60, "units": "Ry"}, "scheme": "QUICKSTEP"}, "hash": "4178802015180711158523341789665647106270915003175636797223149523631617352424712633254110378226507537845096425680076758458702686284378534857806338422045244", "id": "MD_41788...
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Cite this dataset Zhang, D., Yi, P., Lai, X., Peng, L., and Li, H. CGM-MLP natcomm2023 screening amorphous carbon train. ColabFit, 2024. https://doi.org/10.60732/f340d1d9

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Dataset Name

CGM-MLP natcomm2023 screening amorphous carbon train

Description

2558 structures selected from the GAP-20 database. 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.

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

2558

Number of atoms

168066

Elements included

C

Properties included

energy, atomic forces, cauchy stress


Usage

  • ds.parquet : Aggregated dataset information.
  • co/ directory: Configuration rows each include a structure, calculated properties, and metadata.
  • cs/ directory : Configuration sets are subsets of configurations grouped by some common characteristic. If cs/ does not exist, no configurations sets have been defined for this dataset.
  • cs_co_map/ directory : The mapping of configurations to configuration sets (if defined).

ColabFit Exchange documentation includes descriptions of content and example code for parsing parquet files:

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