DeePMD Training

When to Use

• User wants to train a machine learning interatomic potential
• User has DFT data (energies, forces, stresses) and wants a DP model
• User wants to fine-tune a pretrained DPA-3 model

Prerequisites

1. Training data in DeePMD format (use data/dpdata/SKILL.md to convert from VASP/QE)
2. GPU node available on HPC
3. deepmd-kit installed (dp --version)

Data Directory Structure

data/
├── train/
│   ├── set.000/
│   │   ├── coord.npy       # Atomic coordinates (natoms*3,)
│   │   ├── energy.npy      # Total energy (1,)
│   │   ├── force.npy       # Forces (natoms*3,)
│   │   ├── box.npy         # Cell vectors (9,)
│   │   └── virial.npy      # Stress tensor (optional, 9,)
│   └── type.raw            # Atom type indices
└── valid/
    └── set.000/
        └── ...

Workflow Steps

1. Create workflow

catgo_workflow_engine(action="create", params={"name": "DeePMD DPA-3 training"})

2. Add training task

catgo_workflow_engine(action="add_task", params={
  "workflow_id": "wf_xxx",
  "task_type": "shell",
  "name": "dp_train",
  "command": "dp train input.json 2>&1 | tee train.log",
  "input_files": {
    "input.json": "<training config>"
  },
  "system_name": "dp_model"
})

3. Add freeze step (convert to production model)

catgo_workflow_engine(action="add_task", params={
  "workflow_id": "wf_xxx",
  "task_type": "shell",
  "name": "dp_freeze",
  "command": "dp freeze -o frozen_model.pb",
  "depends_on": ["dp_train"],
  "system_name": "dp_model"
})

DPA-3 Training Config (Recommended)

{
  "model": {
    "type_map": ["Ti", "O"],
    "descriptor": {
      "type": "dpa3",
      "rcut": 6.0,
      "rcut_smth": 0.5,
      "sel": "auto",
      "neuron": [25, 50, 100],
      "n_interaction": 3,
      "n_head": 4
    },
    "fitting_net": {
      "type": "ener",
      "neuron": [240, 240, 240]
    }
  },
  "training": {
    "training_data": {
      "systems": ["./data/train"],
      "batch_size": "auto"
    },
    "validation_data": {
      "systems": ["./data/valid"],
      "batch_size": "auto"
    },
    "numb_steps": 1000000,
    "disp_freq": 1000,
    "save_freq": 10000
  },
  "learning_rate": {
    "type": "exp",
    "start_lr": 1e-3,
    "stop_lr": 1e-8,
    "decay_steps": 5000
  },
  "loss": {
    "type": "ener",
    "start_pref_e": 0.02,
    "limit_pref_e": 1.0,
    "start_pref_f": 1000,
    "limit_pref_f": 1.0,
    "start_pref_v": 0.0,
    "limit_pref_v": 0.0
  }
}

se_e2_a Training Config (Legacy)

Replace the descriptor block:

"descriptor": {
  "type": "se_e2_a",
  "rcut": 6.0,
  "rcut_smth": 0.5,
  "sel": [46, 92],
  "neuron": [25, 50, 100],
  "axis_neuron": 16
}

sel must list the max neighbor count per element type. Use dp neighbor-stat to determine.

Fine-Tuning from Pretrained Model

# Download pretrained model (e.g., from AIS Square)
# Then fine-tune:
dp train input.json --finetune pretrained.pb 2>&1 | tee finetune.log

Reduce numb_steps to 100000-200000 and start_lr to 1e-4 for fine-tuning.

Parameter Guidance

Parameter	Typical value	Notes
rcut	6.0-9.0 A	Interaction cutoff; larger = more accurate but slower
sel	auto or [N1, N2]	Max neighbors per type; use dp neighbor-stat
numb_steps	500K-2M	More data needs more steps
start_lr	1e-3	Learning rate; reduce for fine-tuning
batch_size	auto	Let DeePMD choose based on system size
start_pref_f	1000	Force weight starts high, decays to limit_pref_f

Common Pitfalls

1. Insufficient training data — need at least 1000-5000 frames for a reliable model. More diverse configs = better.
2. sel too small — if max neighbors exceeds sel, training crashes. Always run dp neighbor-stat first.
3. Overfitting — monitor validation loss. If train loss drops but valid loss plateaus, stop training or add more data.
4. Missing virial — if training data has no stress tensor, set start_pref_v = 0, limit_pref_v = 0.
5. Mixed element sets — type_map must be consistent across all training systems and inference.
6. Forgetting to freeze — the checkpoint directory is not the production model. Run dp freeze to create a portable .pb file.