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NEP

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NEP
machine-learning-potential
materials-science
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  1. README.md +75 -0
  2. data/MatPL/LiSiC/pwdata/C2/valid/virials.npy +3 -0
  3. data/MatPL/LiSiC/pwdata/C448/train/atom_type.npy +3 -0
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  11. data/MatPL/LiSiC/pwdata/C448/valid/atom_type.npy +3 -0
  12. data/MatPL/LiSiC/pwdata/C448/valid/ei.npy +3 -0
  13. data/MatPL/LiSiC/pwdata/C448/valid/energies.npy +3 -0
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  16. data/MatPL/LiSiC/pwdata/C448/valid/lattice.npy +3 -0
  17. data/MatPL/LiSiC/pwdata/C448/valid/position.npy +3 -0
  18. data/MatPL/LiSiC/pwdata/C448/valid/virials.npy +3 -0
  19. data/MatPL/LiSiC/pwdata/C448Li75/train/atom_type.npy +3 -0
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  21. data/MatPL/LiSiC/pwdata/C448Li75/train/energies.npy +3 -0
  22. data/MatPL/LiSiC/pwdata/C448Li75/train/forces.npy +3 -0
  23. data/MatPL/LiSiC/pwdata/C448Li75/train/image_type.npy +3 -0
  24. data/MatPL/LiSiC/pwdata/C448Li75/train/lattice.npy +3 -0
  25. data/MatPL/LiSiC/pwdata/C448Li75/train/position.npy +3 -0
  26. data/MatPL/LiSiC/pwdata/C448Li75/train/virials.npy +3 -0
  27. data/MatPL/LiSiC/pwdata/C448Li75/valid/atom_type.npy +3 -0
  28. data/MatPL/LiSiC/pwdata/C448Li75/valid/ei.npy +3 -0
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  31. data/MatPL/LiSiC/pwdata/C448Li75/valid/image_type.npy +3 -0
  32. data/MatPL/LiSiC/pwdata/C448Li75/valid/lattice.npy +3 -0
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  38. data/MatPL/LiSiC/pwdata/C64Si32/train/forces.npy +3 -0
  39. data/MatPL/LiSiC/pwdata/C64Si32/train/image_type.npy +3 -0
  40. data/MatPL/LiSiC/pwdata/C64Si32/train/lattice.npy +3 -0
  41. data/MatPL/LiSiC/pwdata/C64Si32/train/position.npy +3 -0
  42. data/MatPL/LiSiC/pwdata/C64Si32/train/virials.npy +3 -0
  43. data/MatPL/LiSiC/pwdata/C64Si32/valid/atom_type.npy +3 -0
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  50. data/MatPL/LiSiC/pwdata/C64Si32/valid/virials.npy +3 -0
README.md ADDED
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+ ---
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+ license: gpl-3.0
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+ tags:
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+ - MatPL
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+ - NEP
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+ - machine-learning-potential
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+ - materials-science
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+ - pwmat
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+ - pwmlff
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+ - extxyz
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+ language:
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+ - en
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+ ---
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+
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+ <p align="center">
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+ <strong>
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+ <span style="font-size: 30px;">NEP</span>
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+ </strong>
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+ </p>
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+
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+ ## Dataset Description
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+
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+ The NEP dataset provides example data for training materials potentials with `OneScience-Group/NEP`. It includes several materials systems, such as AuAg, Cu, HfO2, and LiSiC, in `pwmat/movement`, `pwmlff/npy`, and `extxyz` formats. The dataset contains atomic coordinates, cell information, energies, and forces for different configurations of these systems. It is intended as a standard benchmark for learning and validating machine-learning interatomic potential methods, including NEP.
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+
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+ The current package contains approximately 636 data and metadata files totaling about 38 MB.
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+
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+ ## Supported Tasks
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+
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+ This standardized data repository packages the complete official MatPL examples for four materials systems. It can be used for:
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+
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+ - Training and validating machine-learning interatomic potentials
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+ - Fitting potential energy surfaces for materials simulations
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+ - Functional testing and benchmarking of the NEP framework
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+
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+ ## Dataset Formats and Structure
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+
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+ Data files are stored under `data/MatPL/` and grouped by materials system:
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+
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+ | Path | Format | Shape/content | Description |
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+ | --- | --- | --- | --- |
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+ | `data/MatPL/AuAg/AuAg-5762.xyz` | extxyz | `[N_frames, N_atoms]` | Configurations for the AuAg system |
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+ | `data/MatPL/Cu/pwdata/0_300_MOVEMENT` | pwmat/movement | `[N_frames, N_atoms]` | Cu training data at 300 K |
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+ | `data/MatPL/Cu/pwdata/1_500_MOVEMENT` | pwmat/movement | `[N_frames, N_atoms]` | Cu training data at 500 K |
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+ | `data/MatPL/Cu/pwdata/valid_movement` | pwmat/movement | `[N_frames, N_atoms]` | Cu validation data |
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+ | `data/MatPL/HfO2/pwdata/init_*/train/atom_type.npy` | npy | `[N_atoms]` | Atomic species for the HfO2 system |
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+ | `data/MatPL/HfO2/pwdata/init_*/train/energies.npy` | npy | `[N_frames]` | Total configuration energies for the HfO2 system |
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+ | `data/MatPL/HfO2/pwdata/init_*/train/forces.npy` | npy | `[N_frames, N_atoms×3]` | Atomic forces for the HfO2 system |
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+ | `data/MatPL/HfO2/pwdata/init_*/train/image_type.npy` | npy | `[N_frames]` | Configuration types for the HfO2 system |
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+ | `data/MatPL/HfO2/pwdata/init_*/train/lattice.npy` | npy | `[N_frames, 9]` | Cell matrices for the HfO2 system |
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+ | `data/MatPL/HfO2/pwdata/init_*/train/position.npy` | npy | `[N_frames, N_atoms×3]` | Atomic coordinates for the HfO2 system |
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+ | `data/MatPL/HfO2/pwdata/init_*/valid/` | npy | Same as above | HfO2 validation-data shards |
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+ | `data/MatPL/LiSiC/pwdata/init_*/train/` | npy | Same as above | LiSiC training-data shards |
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+ | `data/MatPL/LiSiC/pwdata/init_*/valid/` | npy | Same as above | LiSiC validation-data shards |
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+ | `metadata/sha256_manifest.txt` | SHA-256 | Integrity manifest | File-integrity checksums |
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+
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+ ## Downloading the Dataset
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+
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+ ```bash
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+ hf download --dataset OneScience-Group/NEP --local-dir ./data
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+ ```
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+
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+ ## Official OneScience Resources
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+
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+ | Platform | OneScience Main Repository | Skills Repository |
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+ | --- | --- | --- |
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+ | Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
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+ | GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |
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+
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+ ## Limitations and License
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+
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+ This dataset consists of the official MatPL example data. No new samples were generated, and the original data contents were not modified.
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+
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+ - **Original source:** Official MatPL repository
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+ - **Model card metadata:** `gpl-3.0`, normalized from the source `GPLv3` declaration
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+ - **Source terms:** Before redistribution or production use, review the licensing and provenance information provided by the original MatPL data sources and the OneScience repository. These source-specific terms remain authoritative for any component with separate licensing.
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