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name deepmd-train
description 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, DPA3, and DPA4/SeZM, run `dp train`, monitor learning curves, freeze checkpoints, or test trained models. Start with model selection and read only the selected model reference under `models/` when model-specific configuration is needed.
compatibility Requires deepmd-kit installed. The selected backend and model may require PyTorch, TensorFlow, JAX, Paddle, GPU support, or custom OP libraries.
license LGPL-3.0-or-later
metadata
author version repository
iProzd
1.2

DeePMD-kit Training

Use this skill to guide DeePMD-kit model training without loading every model-specific recipe up front. The workflow is intentionally progressive:

  1. Understand the user's data, target accuracy, compute budget, and deployment backend.
  2. Choose an appropriate model family.
  3. Read only the reference file for the selected model under models/.
  4. Generate or edit input.json, run training, monitor, freeze, and test.

Progressive disclosure protocol

Do not start by reading every model document. First classify the request:

  • If the user already named a model, read only that model reference.
  • If the user asks for a recommendation, collect the decision inputs below, choose a model, then read only the selected reference.
  • If model-specific parameters are not needed yet, stay in this top-level workflow.

Available model references:

Model reference Read when
models/se-e2-a.md The user wants a classical DeepPot-SE baseline, broad compatibility, or a smaller/established production model.
models/dpa3.md The user wants a high-accuracy DPA3/LAM workflow, large/diverse datasets, dynamic neighbor selection, or pretrained DPA3-style training.
models/dpa4.md The user wants the PyTorch-only DPA4/SeZM SO(3)-equivariant architecture and its .pt2 deployment path.

Model selection

Ask only for missing information that changes the choice. Prefer reasonable defaults when the answer is obvious from context.

Key inputs:

  • Data format and size: deepmd/npy, deepmd/hdf5, mixed type, number of systems/frames/elements.
  • Target: quick baseline, production accuracy, large atomic model, transfer/fine-tuning, or deployment in MD.
  • Compute: CPU/GPU, available memory, single-node vs. distributed training.
  • Backend/deployment: PyTorch/TensorFlow/JAX/Paddle training; LAMMPS, Python inference, or other downstream use.
  • Labels: energy/force only or also virial/stress.
  • System diversity: single chemistry/phase vs. diverse multi-domain datasets.

Recommended defaults:

  • Choose se_e2_a for a robust baseline, small to medium systems, compatibility-focused workflows, or when compute is limited.
  • Choose DPA3 for high accuracy on diverse datasets, LAM-style training, or when the user explicitly asks for DPA3, DPA-3, LiGS, dynamic neighbor selection, or pretrained DPA3 variants.
  • Choose DPA4/SeZM when the user explicitly requests it or wants its SO(3)-equivariant message-passing architecture and accepts a GPU-oriented, PyTorch-only workflow.

Common workflow

1. Confirm environment

dp --version

For PyTorch training, use dp --pt ...; for TensorFlow, use dp ...; for other backends, confirm the installed backend first.

2. Confirm training data

Training data should be in DeePMD format, typically deepmd/npy or deepmd/hdf5. If the user has raw electronic-structure outputs, convert them first with dpdata before writing the training input.

Minimum information needed to build input.json:

  • type_map
  • training system paths
  • validation system paths
  • whether virial labels are present and should be trained
  • target number of steps or accuracy/time budget
  • model choice

3. Read the selected model reference

After selecting a model, read the corresponding file under models/ and apply its model-specific configuration, hyperparameters, and caveats.

4. Train

dp --pt train input.json

Use the backend-specific command if not using PyTorch.

Restart from a checkpoint when needed:

dp --pt train input.json --restart model.ckpt.pt

5. Monitor

Training progress is usually written to lcurve.out. Check for:

  • decreasing validation RMSE
  • NaN or exploding losses
  • train/validation divergence
  • learning-rate schedule behaving as expected

6. Freeze and test

Read the selected model reference before choosing the output format. For conventional PyTorch models, a typical flow is:

dp --pt freeze -o model.pth
dp test -m model.pth -s /path/to/test_system -n 30

DPA4/SeZM checkpoints instead freeze to .pt2; follow models/dpa4.md. Adjust the backend and output format for other model families.

Agent checklist

  • Model was selected before reading model-specific details.
  • Only the selected model reference was loaded.
  • Training/validation data paths exist or are clearly marked as placeholders.
  • type_map matches the data and model/pretrained checkpoint.
  • Virial loss is enabled only when virial labels are available and desired.
  • Backend command matches the selected model and installed DeePMD-kit environment.
  • The generated input.json is valid JSON.
  • Training was monitored via lcurve.out or equivalent logs.
  • Final model was frozen and tested when requested.

References