Quickstart
This guide gets you from installation to first results.
1. Install
Python 3.10–3.12
Recommended: create a fresh environment
conda create -n nepkit python=3.10
conda activate nepkit
pip install NepTrainKit
Windows portable: download NepTrainKit.win32.zip from Releases and run the executable.
2. Launch
nepkit
# or
NepTrainKit
3. NEP Dataset Display
Import data via the top‑left Open button or drag‑and‑drop.
Supported imports:
train.xyz+ corresponding*.outfilesnep.txt(optional; uses NEP89 if absent) +train.xyzDeepMD directory (auto‑detected)
Interact with plots, search by
Config_typeor formula, select, delete, and export:Export menu → “Export Selected Structures” for chosen frames
Save button exports
export_remove_model.xyzandexport_good_model.xyz
4. Make Dataset
Drag structures (XYZ/POSCAR/CIF) into the window or use Open.
Build a pipeline with cards; use groups to branch/merge; add FPS filter if needed.
Export to
make_dataset.xyzwhen done.Save/Load card configurations as JSON to reuse pipelines.
5. Data Management
Organize datasets into Projects and Models (versions), with notes and tags.
Right‑click for New/Modify/Delete, Open Folder, and Tag management.
Press
Ctrl+Ffor advanced search.
6. Settings
Choose plotting force mode (Raw vs Norm) and canvas engine (PyQtGraph vs Vispy).
NEP Backend: select CPU/GPU/Auto for NEP calculations; Auto tries GPU first and falls back to CPU
GPU Batch Size: adjust the number of frames per GPU slice to balance speed and memory
Enable Auto loading, adjust covalent radius threshold, sorting, and menu grouping.
Check app updates and NEP89 model, open help and feedback.
Note:
GPU backend requires a compatible NVIDIA driver and CUDA 12.4 runtime. If you see “CUDA driver version is insufficient for CUDA runtime version”, switch NEP Backend to CPU in Settings.
7. Tips
Use Vispy for large scenes if your GPU supports OpenGL.
Toggle formula search to match by composition rather than tags.
Use the structure toolbar to export descriptors or mark non‑physical bonds.