| --- |
| |
| tags: |
| - DMC |
| - MACE |
| - molecular dynamics |
| - extxyz |
| - carbonate |
| - XTB |
| language: |
| - en |
| - zh |
| license: mit |
| --- |
| <p align="center"> |
| <strong> |
| <span style="font-size: 30px;">DMC</span> |
| </strong> |
| </p> |
| |
| ## 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 --dataset OneScience-Sugon/DMC --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 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 |
|
|
