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OneScience-Group/water

water Dataset Description The water dataset is an extxyz dataset for potential energy surface training and evaluation examples involving water systems. It contains atomic coordinates, unit-cell information, energies, and forces for water molecular systems in different configurations and is a standard benchmark dataset for developing and validating machine-learned interatomic potential methods such as MACE. The original complete file dataset_1593.xyz contains 1,593… See the full description on the dataset page: https://huggingface.co/datasets/OneScience-Group/water.

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Dataset Card

water

Dataset Description

The water dataset is an extxyz dataset for potential energy surface training and evaluation examples involving water systems. It contains atomic coordinates, unit-cell information, energies, and forces for water molecular systems in different configurations and is a standard benchmark dataset for developing and validating machine-learned interatomic potential methods such as MACE.

The original complete file dataset_1593.xyz contains 1,593 frames with 192 atoms per frame. To match the original MACE water configuration, this package follows the logic of the original gene_xyz.py to derive the first 1,393 frames from dataset_1593.xyz as the water_train.xyz training set and the final 200 frames as the water_test.xyz test set.

The original water training set comes from BingqingCheng/ab-initio-thermodynamics-of-water.

The current data package contains approximately 4 files totaling approximately 150 MB.

Supported Tasks

This standardized data repository organizes the complete data package for the MACE water example, including the full dataset, training set, and test set, to support:

—Training and validation of potential energy surface models for water 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/water/ directory:

File Path	Format	Shape / Content	Description
`water/dataset_1593.xyz`	extxyz	`[1593, 192]`	Full dataset, 1,593 frames with 192 atoms per frame
`water/water_train.xyz`	extxyz	`[1393, 192]`	Training set, the first 1,393 frames with 192 atoms per frame
`water/water_test.xyz`	extxyz	`[200, 192]`	Test set, the final 200 frames with 192 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 TotEnergy, cutoff, nneightol, pbc, Lattice, and Properties. Each atom line contains the element, coordinates, force, and Z.

Field	Type	Shape	Description
`species`	str	`[N_atoms]`	Element symbols; the current set of elements is H and O
`pos`	float	`[N_atoms, 3]`	Atomic coordinates in Å
`force`	float	`[N_atoms, 3]`	Atomic forces in eV/Å
`Z`	int	`[N_atoms]`	Atomic numbers
`TotEnergy`	float	`[1]`	Total configuration energy in eV
`Lattice`	float	`[3, 3]`	Periodic cell matrix
`pbc`	bool	`[3]`	Periodic boundary conditions

How to Use the Dataset

Download the dataset:

bash

hf download OneScience-Group/water --repo-type dataset

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 MACE water example data. The train/test split was derived from the full dataset using the logic of the original gene_xyz.py; no new samples were generated, and the original full dataset content was not altered. Refer to the original water data source and the information provided by the OneScience repository and ModelScope page for licensing terms.

When using the original water dataset, cite:

—Cheng, B.; Engel, E. A.; Behler, J.; Dellago, C.; Ceriotti, M. Ab Initio Thermodynamics of Liquid Water and Ice. arXiv preprint 2018. https://arxiv.org/abs/1811.08630