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PO_1001287509428830657676566
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2025-04-16T15:44:22
DS_sz9me9r61581_0
1
VASP
DFT/optB88-vdW
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2025-04-16T21:52:07
DS_sz9me9r61581_0
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2025-04-16T17:10:53
DS_sz9me9r61581_0
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2025-04-16T20:15:15
DS_sz9me9r61581_0
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2025-04-16T21:51:46
DS_sz9me9r61581_0
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VASP
DFT/optB88-vdW
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2025-04-16T21:13:01
DS_sz9me9r61581_0
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2025-04-16T16:06:34
DS_sz9me9r61581_0
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2025-04-16T17:50:49
DS_sz9me9r61581_0
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2025-04-16T18:30:57
DS_sz9me9r61581_0
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2025-04-16T17:07:03
DS_sz9me9r61581_0
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2025-04-16T21:21:56
DS_sz9me9r61581_0
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2025-04-17T17:56:16
DS_sz9me9r61581_0
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2025-04-16T20:29:09
DS_sz9me9r61581_0
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DFT/optB88-vdW
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2025-04-16T17:04:26
DS_sz9me9r61581_0
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2025-04-16T21:44:51
DS_sz9me9r61581_0
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2025-04-16T18:44:20
DS_sz9me9r61581_0
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2025-04-17T17:56:16
DS_sz9me9r61581_0
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DFT/optB88-vdW
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2025-04-16T16:25:16
DS_sz9me9r61581_0
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2025-04-17T18:26:41
DS_sz9me9r61581_0
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VASP
DFT/optB88-vdW
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2025-04-16T17:39:34
DS_sz9me9r61581_0
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DFT/optB88-vdW
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2025-04-16T16:50:16
DS_sz9me9r61581_0
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DFT/optB88-vdW
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2025-04-16T17:29:12
DS_sz9me9r61581_0
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DFT/optB88-vdW
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2025-04-16T21:29:37
DS_sz9me9r61581_0
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2025-04-16T15:43:44
DS_sz9me9r61581_0
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DFT/optB88-vdW
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2025-04-16T16:57:01
DS_sz9me9r61581_0
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2025-04-16T15:23:14
DS_sz9me9r61581_0
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2025-04-16T15:43:17
DS_sz9me9r61581_0
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2025-04-16T16:34:13
DS_sz9me9r61581_0
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VASP
DFT/optB88-vdW
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2025-04-17T18:48:22
DS_sz9me9r61581_0
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2025-04-16T22:03:29
DS_sz9me9r61581_0
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2025-04-16T20:08:41
DS_sz9me9r61581_0
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data/MD/2229/MD_9321007856532242169222229.json
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2025-04-16T15:14:03
DS_sz9me9r61581_0
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VASP
DFT/optB88-vdW
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data/MD/2229/MD_9321007856532242169222229.json
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{"config_type": "Amorphous_Bulk", "hash": "9321007856532242169222229034018050775805146400287982340713513919047442689138559910921405913858210882301955649016535905049146021089235359446976918785169490", "id": "MD_9321007856532242169222229"}
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2025-04-16T21:56:50
DS_sz9me9r61581_0
1
VASP
DFT/optB88-vdW
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C63
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data/MD/9501/MD_1207782181610877799959501.json
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End of preview. Expand in Data Studio

Cite this dataset Rowe, P., Deringer, V. L., Gasparotto, P., Csányi, G., and Michaelides, A. Carbon GAP JCP 2020 train. ColabFit, 2023. https://doi.org/10.60732/9d095830

This dataset has been curated and formatted for the ColabFit Exchange

This dataset is also available on the ColabFit Exchange:

https://materials.colabfit.org/id/DS_sz9me9r61581_0

Visit the ColabFit Exchange to search additional datasets by author, description, element content and more.

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Dataset Name

Carbon GAP JCP 2020 train

Description

Training data generated for GAP-20. GAP-20 describes the properties of the bulk crystalline and amorphous phases, crystal surfaces, and defect structures with an accuracy approaching that of direct ab initio simulation, but at a significantly reduced cost. The final potential is fitted to reference data computed using the optB88-vdW density functional theory (DFT) functional.

Dataset authors

Patrick Rowe, Volker L. Deringer, Piero Gasparotto, Gábor Csányi, Angelos Michaelides

Publication

https://doi.org/10.1063/5.0005084

Original data link

https://www.repository.cam.ac.uk/handle/1810/307452

License

CC-BY-4.0

Number of unique molecular configurations

6088

Number of atoms

400275

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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