OneScience-Group/nanotube

nanotube

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:

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.