dpdata

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Convert between computational chemistry data formats using dpdata. Handles VASP, QE, CP2K, Gaussian, LAMMPS, and DeePMD formats. Essential for preparing ML potential training data.

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dpdata — Format Conversion

When to Use

User needs to convert DFT calculation outputs to DeePMD training format
User wants to convert between VASP, QE, CP2K, Gaussian, LAMMPS formats
User needs to merge, filter, or split trajectory data
User is preparing training data for machine learning potentials

Prerequisites

dpdata installed (pip install dpdata, or python -c "import dpdata")

Supported Formats

Format	Read	Write	Key
VASP OUTCAR	Yes	-	`vasp/outcar`
VASP POSCAR/CONTCAR	Yes	Yes	`vasp/poscar`
VASP XML	Yes	-	`vasp/xml`
QE pw.x output	Yes	-	`qe/pw/scf`
CP2K output	Yes	-	`cp2k/output`
Gaussian log	Yes	-	`gaussian/log`
LAMMPS dump	Yes	Yes	`lammps/dump`
LAMMPS data	Yes	Yes	`lammps/lmp`
DeePMD raw	Yes	Yes	`deepmd/raw`
DeePMD npy	Yes	Yes	`deepmd/npy`
ExtXYZ	Yes	Yes	`extxyz`

Workflow Steps

1. Convert VASP OUTCAR to DeePMD

catgo_workflow_engine(action="add_task", params={
  "workflow_id": "wf_xxx",
  "task_type": "shell",
  "name": "convert_data",
  "command": "python convert.py",
  "input_files": {
    "convert.py": "import dpdata\nd = dpdata.LabeledSystem('OUTCAR', fmt='vasp/outcar')\nd.to('deepmd/npy', 'training_data')"
  },
  "system_name": "data_prep"
})

Common Conversion Scripts

VASP to DeePMD (with train/valid split)

import dpdata
import numpy as np

# Load all frames from OUTCAR
d = dpdata.LabeledSystem("OUTCAR", fmt="vasp/outcar")
print(f"Loaded {len(d)} frames")

# Random split: 90% train, 10% valid
indices = np.random.permutation(len(d))
n_train = int(0.9 * len(d))

d_train = d.sub_system(indices[:n_train])
d_valid = d.sub_system(indices[n_train:])

d_train.to("deepmd/npy", "data/train")
d_valid.to("deepmd/npy", "data/valid")
print(f"Train: {len(d_train)}, Valid: {len(d_valid)}")

Multiple OUTCARs to single dataset

import dpdata
from pathlib import Path

d = None
for outcar in Path(".").rglob("OUTCAR"):
    sys = dpdata.LabeledSystem(str(outcar), fmt="vasp/outcar")
    d = sys if d is None else d + sys

print(f"Total frames: {len(d)}")
d.to("deepmd/npy", "merged_data")

QE to DeePMD

import dpdata
d = dpdata.LabeledSystem("relax.out", fmt="qe/pw/scf")
d.to("deepmd/npy", "training_data")

LAMMPS dump to ExtXYZ

import dpdata
d = dpdata.System("dump.lammpstrj", fmt="lammps/dump",
                  type_map=["Ti", "O"])
d.to("extxyz", "trajectory.xyz")

Filter by energy/force

import dpdata
import numpy as np

d = dpdata.LabeledSystem("OUTCAR", fmt="vasp/outcar")

# Remove frames with max force > 10 eV/Ang (likely unconverged)
mask = []
for i in range(len(d)):
    max_f = np.max(np.abs(d["forces"][i]))
    mask.append(max_f < 10.0)

d_clean = d.sub_system(np.where(mask)[0])
print(f"Kept {len(d_clean)}/{len(d)} frames")

CLI Usage

# Quick convert
dpdata convert OUTCAR vasp/outcar deepmd/npy training_data

# System info
dpdata info OUTCAR vasp/outcar

Parameter Guidance

Parameter	Notes
`fmt`	Format string — must match exactly (case-sensitive)
`type_map`	Required for LAMMPS formats — maps type indices to element symbols
`begin` / `end` / `step`	Frame selection for large trajectories

Common Pitfalls

Missing type_map for LAMMPS — LAMMPS dump files have numeric types, not element names. Always provide type_map.
Unconverged frames — VASP OUTCARs may contain unconverged ionic steps. Filter by force magnitude before training.
Mixed element order — when merging data from different calculations, ensure consistent element ordering.
Large memory for big trajectories — dpdata loads all frames into memory. For >10K frames, process in chunks.
Units — dpdata converts to eV/Angstrom internally. LAMMPS real units are auto-converted.