deepmd-inference

name	deepmd-inference
description	Run DeePMD-kit inference to predict energies, forces, and stresses using a trained DP model. Also covers model evaluation and testing.
compatibility	Requires deepmd-kit installed. A frozen model (.pb) file is needed.
catalog-hidden	true

DeePMD Inference

When to Use

• User wants to predict energy/forces for a structure using a trained DP model
• User wants to evaluate model accuracy against DFT reference data
• User wants to use a DP model as an ASE calculator for optimization or NEB

Prerequisites

1. A frozen DeePMD model file (.pb or .savedmodel)
2. deepmd-kit installed (dp --version)
3. Structure to predict on, or test data in dpdata format

Workflow Steps

1. Model Testing (against reference data)

catgo_workflow_engine(action="add_task", params={
  "workflow_id": "wf_xxx",
  "task_type": "shell",
  "name": "dp_test",
  "command": "dp test -m frozen_model.pb -s ./data/test -n 100 -d test_results 2>&1 | tee test.log",
  "system_name": "dp_eval"
})

This outputs RMSE for energy, forces, and virial.

2. Single Structure Prediction (Python)

catgo_workflow_engine(action="add_task", params={
  "workflow_id": "wf_xxx",
  "task_type": "shell",
  "name": "dp_predict",
  "command": "python predict.py",
  "input_files": {
    "predict.py": "<script content>"
  },
  "system_name": "dp_predict"
})

Python Script — Single Prediction

from deepmd.infer import DeepPot
from ase.io import read
import numpy as np

dp = DeepPot("frozen_model.pb")
atoms = read("structure.vasp")

coord = atoms.get_positions().reshape(1, -1)
cell = atoms.get_cell().array.reshape(1, -1)
atype = [dp.get_type_map().index(s) for s in atoms.get_chemical_symbols()]

energy, force, virial = dp.eval(coord, cell, atype)

print(f"Energy: {energy[0][0]:.6f} eV")
print(f"Max force: {np.max(np.abs(force)):.6f} eV/Ang")

Python Script — ASE Calculator

from deepmd.calculator import DP
from ase.io import read, write
from ase.optimize import BFGS

atoms = read("structure.vasp")
atoms.calc = DP(model="frozen_model.pb")

# Single point
energy = atoms.get_potential_energy()
forces = atoms.get_forces()
print(f"Energy: {energy:.6f} eV")

# Optimization
opt = BFGS(atoms, trajectory="opt.traj")
opt.run(fmax=0.01)
write("optimized.vasp", atoms)

dp test Output Format

Energy RMSE        : 1.234e-03 eV/atom
Force  RMSE        : 2.345e-02 eV/Ang
Virial RMSE        : 3.456e-01 eV/cell

Acceptable thresholds:

• Energy: < 5 meV/atom
• Force: < 100 meV/Ang (< 50 meV/Ang for high accuracy)
• Virial: < 1 kbar

Model Compression (for faster inference)

dp compress -i frozen_model.pb -o compressed_model.pb

Compressed models are 3-10x faster with minimal accuracy loss. Always compress before production MD.

Parameter Guidance

Parameter	Notes
-m	Path to frozen model (.pb)
-s	Path to test data directory (dpdata format)
-n	Number of test frames (default: all)
-d	Output directory for detailed results
--atomic	Output per-atom energy decomposition

Common Pitfalls

1. type_map mismatch — the element order in inference must match training. Check with dp show-type-map frozen_model.pb.
2. Unfrozen model — dp test and ASE calculator need a frozen .pb file, not the training checkpoint directory.
3. Extrapolation — DP models are unreliable outside the training data distribution. Check the model deviation.
4. Model deviation — for production use, train 4 models with different seeds and compute max_devi_f to detect extrapolation.
5. Memory for large systems — DP inference on 10K+ atoms can exceed GPU memory. Use --batch-size or CPU inference.