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float64
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2025-04-16T16:45:11
DS_vgy50b4qz4p7_0
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CCu144
CCu144
A144B
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[ "Carbon_Cluster_Training_Set_170", "C_G_M_-_M_L_P_-_C_u_-_C_Training_Dataset_deposition__deposition_225", "C_G_M_-_M_L_P_-_C_u_-_C_-_O_Training_Dataset_deposition__deposition_225" ]
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data/MD/3341/MD_4178802015180711158523341.json
data/MD/2360/MD_5085563091843178930982360.json
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2025-04-16T20:24:05
DS_vgy50b4qz4p7_0
1
CP2K
DFT-PBE+D3
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C5Cu144
C5Cu144
A144B5
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2025-04-16T18:46:05
DS_vgy50b4qz4p7_0
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C6Cu144
CCu24
A24B
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PO_1003032577527352374443247
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2025-04-16T17:47:41
DS_vgy50b4qz4p7_0
1
CP2K
DFT-PBE+D3
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C2Cu288
CCu144
A144B
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PO_4936874523486561708210289
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2025-04-16T20:09:39
DS_vgy50b4qz4p7_0
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C18Cu144
CCu8
A8B
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PO_4930410931228159131807475
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2025-04-16T16:10:12
DS_vgy50b4qz4p7_0
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C7Cu144
C7Cu144
A144B7
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2025-04-16T20:10:51
DS_vgy50b4qz4p7_0
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2025-04-16T21:23:51
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2025-04-16T18:42:12
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2025-04-16T18:16:02
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2025-04-17T18:48:22
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2025-04-16T19:57:19
DS_vgy50b4qz4p7_0
1
CP2K
DFT-PBE+D3
-925.315661
[ [ -0.065038, -0.066928, -1.012542 ], [ -0.138796, -0.246756, 0.823819 ], [ 0.670183, -0.299403, 1.78733 ], [ -1.257574, -0.665395, 1.209537 ], [ 0.920939, -0.516888, 1.266272 ], [ 0.680012, 1.310616, 1.103872 ], [ -0...
[ [ 0.052239, -0.054521, 0.030541 ], [ -0.054521, 0.174797, 0.00279 ], [ 0.030541, 0.00279, 0.104252 ] ]
true
null
null
null
null
null
12.710502
2.010632
null
CO_8950822967257465774076996
8950822967257465774076996562795304725364544283894452448369292258302196555813105854649669715268083649653274512503187886008716263227824992352872317871945203
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[ [ 15.027817947, 0, 0 ], [ 0, 17.3526203058, 0 ], [ 0, 0, 60 ] ]
[ [ 0.0317175, 17.2791, 7.41351 ], [ 0.101917, 1.44925, 9.38587 ], [ 14.9185, 3.02596, 11.2177 ], [ 1.45043, 2.20423, 7.09069 ], [ 1.29655, 3.71477, 9.28623 ], [ 10.2094, 15.6043, 11.0042 ], [ 0.117433, 4.19886, ...
[ true, true, true ]
C55Cu144
C55Cu144
A144B55
[ "C", "Cu" ]
[ 0.27638190954773867, 0.7236180904522613 ]
[ 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, ...
199
2
3
[ 1, 1, 1 ]
[ "C_G_M_-_M_L_P_-_C_u_-_C_Training_Dataset_deposition__deposition_1074", "C_G_M_-_M_L_P_-_C_u_-_C_-_O_Training_Dataset_deposition__deposition_1074" ]
null
data/MD/3341/MD_4178802015180711158523341.json
data/MD/2360/MD_5085563091843178930982360.json
null
{"config_type": "deposition", "hash": "5085563091843178930982360100539406021215696347708504656720957170424303591863714685556596531403026063781709596599516565635016083334750335589969203795829992", "id": "MD_5085563091843178930982360"}
{"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 Cu-C deposition. ColabFit, 2024. https://doi.org/10.60732/c3b4e684

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Dataset Name

CGM-MLP natcomm2023 Cu-C deposition

Description

Training simulations from CGM-MLP_natcomm2023 of carbon deposition on a 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.

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

1177

Number of atoms

204591

Elements included

C, Cu

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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