Datasets:
License:
| license: apache-2.0 | |
| # User-Defined Tags | |
| tags: | |
| - nanotube | |
| - MACE | |
| - carbon nanotube | |
| - extxyz | |
| - machine learning potential | |
| language: | |
| - en | |
| - zh | |
| <p align="center"> | |
| <strong> | |
| <span style="font-size: 30px;">nanotube</span> | |
| </strong> | |
| </p> | |
| ## Dataset Description | |
| The nanotube dataset is an extxyz dataset for carbon nanotube training and evaluation examples. It contains atomic coordinates, energies, and force data for carbon nanotube systems in different configurations and is a standard benchmark dataset for developing and validating machine-learned interatomic potential methods. | |
| The training file `nanotube_large.xyz` contains 4,000 frames, while the test file `nanotube_test.xyz` contains 1,032 frames. Each frame contains 370 atoms, and the set of elements is C and H. | |
| The current data package contains approximately 3 files. | |
| ## Supported Tasks | |
| This standardized data repository organizes the complete data package for the MACE nanotube example, including training and test sets, to support: | |
| - Training and validation of potential energy surface models for carbon nanotube systems | |
| - Potential energy surface fitting for molecular dynamics simulations | |
| - Functional testing and benchmark evaluation of the MACE framework | |
| ## Dataset Format and Structure | |
| The data files are located in the `data/nanotube/` directory: | |
| | File Path | Format | Shape / Content | Description | | |
| |---|---|---|---| | |
| | `nanotube/nanotube_large.xyz` | extxyz | `[4000, 370]` | Training set, 4,000 frames with 370 atoms per frame | | |
| | `nanotube/nanotube_test.xyz` | extxyz | `[1032, 370]` | Test set, 1,032 frames with 370 atoms per frame | | |
| | `metadata/sha256_manifest.txt` | SHA256 | Checksum manifest | File integrity verification | | |
| ### extxyz Data Format | |
| Each configuration consists of an atom-count line, a comment line, and atom lines. The comment line contains `Properties`, `Energy`, and `pbc`. Each atom line contains the element, coordinates, and `forces`. | |
| | Field | Type | Shape | Description | | |
| |---|---|---|---| | |
| | `species` | str | `[N_atoms]` | Element symbols; the current set of elements is C and H | | |
| | `pos` | float | `[N_atoms, 3]` | Atomic coordinates in Å | | |
| | `forces` | float | `[N_atoms, 3]` | Atomic forces in eV/Å | | |
| | `Energy` | float | `[1]` | Total configuration energy in eV | | |
| | `pbc` | bool | `[3]` | Periodic boundary conditions | | |
| ## How to Use the Dataset | |
| This dataset is suitable for multiple molecular dynamics models in the `OneScience-Sugon` repository. | |
| Download the dataset: | |
| ```bash | |
| hf download --dataset OneScience-Sugon/nanotube --local-dir ./data | |
| ``` | |
| ## Official OneScience Information | |
| | Platform | OneScience Main Repository | Skills Repository | | |
| |---|---|---| | |
| | Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills | | |
| | GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills | | |
| ## Limitations and License | |
| This dataset contains nanotube example data; no new samples were generated, and the original data content was not altered. The original data comes from the double-walled nanotube system in the MD22 Benchmark Dataset (sGDML), computed at the DFT-PBE+MBD level (FHI-aims + i-PI). | |
| - **Original source**: http://www.sgdml.org/#datasets | |
| - **Direct download link**: http://www.quantum-machine.org/gdml/repo/datasets/md22_double-walled_nanotube.npz | |
| - **License**: Refer to the original nanotube data source and the information provided by the OneScience repository and ModelScope page | |
| When using the original nanotube dataset, cite: | |
| - Chmiela, S.; Vassilev-Galindo, V.; Unke, O. T.; Kabylda, A.; Sauceda, H. E.; Tkatchenko, A.; Müller, K.-R. Accurate Global Machine Learning Force Fields for Molecules with Hundreds of Atoms. *Science Advances* **2023**, *9*(2), eadf0873. | |