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OneScience-Group/DMC

DMC Dataset Description DMC is a solvent XTB extxyz dataset for introductory training examples. It contains atomic coordinates, energies, and forces for carbonate molecular systems (VC, EC, PC, DMC, EMC, and DEC) in different configurations, and serves as a standard introductory benchmark dataset for learning and validating machine-learning interatomic potential methods. The training file solvent_xtb_train_200.xyz contains 203 frames (200 training configurations + 3… See the full description on the dataset page: https://huggingface.co/datasets/OneScience-Group/DMC.

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DMC

Dataset Description

DMC is a solvent XTB extxyz dataset for introductory training examples. It contains atomic coordinates, energies, and forces for carbonate molecular systems (VC, EC, PC, DMC, EMC, and DEC) in different configurations, and serves as a standard introductory benchmark dataset for learning and validating machine-learning interatomic potential methods.

The training file solvent_xtb_train_200.xyz contains 203 frames (200 training configurations + 3 isolated atom configurations), while the test file solvent_xtb_test.xyz contains 1000 frames. The element set is H, C, and O.

The current data package contains about 3 files.

Supported Tasks

This standardized data repository provides the complete data package for the introductory MACE DMC example, including training and test sets. It can be used for:

—Introductory training and validation of potential energy surface models for carbonate molecular 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/DMC/ directory:

File Path	Format	shape / Content	Description
`DMC/solvent_xtb_train_200.xyz`	extxyz	`[203, N_atoms]`	Training set, 200 training configurations + 3 isolated atom configurations
`DMC/solvent_xtb_test.xyz`	extxyz	`[1000, N_atoms]`	Test set, 1000 frames
`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_xtb, and pbc. Each atom line contains the element, coordinates, optional molID, and forces_xtb.

Field	Type	shape	Description
`species`	str	`[N_atoms]`	Element symbols; the current element set is H, C, and O
`pos`	float	`[N_atoms, 3]`	Atomic coordinates in Å
`molID`	int	`[N_atoms]`	Molecule ID; it may be absent from isolated atom configurations
`forces_xtb`	float	`[N_atoms, 3]`	Atomic forces calculated with XTB, in eV/Å
`energy_xtb`	float	`[1]`	Total configuration energy calculated with XTB, in eV
`pbc`	bool	`[3]`	Periodic boundary conditions

How to Use the Dataset

Download the dataset:

bash

hf download  OneScience-Group/DMC --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 the introductory DMC training example data. No new samples were generated, and the original data content was not modified. The original configuration data come from the 2022 work by Dajnowicz et al. on machine-learning potentials for lithium-ion battery electrolytes, with energies and forces recalculated using XTB (GFN2-xTB).

—Original Paper: Dajnowicz, S.; Agarwal, G.; Stevenson, J. M.; Jacobson, L. D.; Ramezanghorbani, F.; Leswing, K.; Friesner, R. A.; Halls, M. D.; Abel, R. High-Dimensional Neural Network Potential for Liquid Electrolyte Simulations. J. Phys. Chem. B 2022, 126, 6271–6280. https://doi.org/10.1021/acs.jpcb.2c03746
—Original Data Repository: https://github.com/imagdau/Tutorials
—License: The original data do not explicitly declare a license; refer to the original DMC data source, the OneScience repository, and the ModelScope page for applicable terms