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SkillsMP has collected 7 skills from deepmodeling/deepmd-kit. Open a skill to review its source and details.
Showing 7 of 7 collected skills.
Install DeePMD-kit with pip, conda, dp1s, an offline package, Docker, or source code. Use for PyTorch, TensorFlow, JAX, or Paddle on CPU, CUDA, or ROCm, and for backend-enabled or backend-neutral C/C++ interfaces and DeePMD-enabled LAMMPS.
Train DeePMD-kit models with progressive disclosure. Use when the user wants to train a DeePMD-kit potential, prepare an input.json, choose between model families such as se_e2_a/DeepPot-SE and DPA3, run `dp train`, monitor learning curves, freeze…
Fine-tune a DPA3 model in DeePMD-kit using the PyTorch backend. Use when the user wants to adapt a pre-trained DPA3 model to a new downstream dataset. Supports fine-tuning from a self-trained DPA3 model (.pt checkpoint), from a multi-task pre-trained model,…
Run Python inference with DeePMD-kit models using the DeepPot API. Use when the user wants to load a trained/frozen DeePMD model (.pth or .pb) or a built-in pretrained model (e.g., DPA-3.2-5M) in Python, predict energy/force/virial for atomic configurations,…
A tool and knowledge base for running molecular dynamics (MD) simulations in LAMMPS with the DeePMD-kit plugin. It handles input script preparation, ensemble selection (NVE/NVT/NPT), and job execution via `uv` or offline binaries. USE WHEN you need to set up,…
Diagnose gradient flow issues in training, especially for compiled models (torch.compile/make_fx). Systematically isolates which loss components (energy, force, virial) contribute gradients to which parameters, and identifies where the gradient chain breaks.
Guides through adding a new descriptor type to deepmd-kit. Covers implementing in dpmodel (array-API-compatible), wrapping for JAX/pt_expt backends, hard-coding for PT/PD, registering arguments, and writing all required tests.