dpdata-driver

name	dpdata-driver
description	Use dpdata Python Driver plugins to label systems (energies/forces/virials) via System.predict(), list available drivers, and build Driver objects (ase/deepmd/gaussian/sqm/hybrid). Use when working with dpdata Python API (not CLI) and you need driver-based energy/force prediction, plugin registration keys, or examples of using dpdata with ASE calculators or DeePMD models.
license	LGPL-3.0-or-later
compatibility	Requires dpdata or uv for running dpdata
metadata	{"author":"njzjz-bot","version":"1.0"}

dpdata-driver

Use dpdata “driver plugins” to label a dpdata.System (predict energies/forces/virials) and obtain a dpdata.LabeledSystem.

Key idea

• A Driver converts an unlabeled System into a LabeledSystem by computing:
  • energies (required)
  • forces (optional but common)
  • virials (optional)

In dpdata, this is exposed as:

• System.predict(*args, driver="dp", **kwargs) -> LabeledSystem

driver can be:

• a string key (plugin name), e.g. "ase", "dp", "gaussian"
• a Driver object, e.g. Driver.get_driver("ase")(...)

List supported driver keys (runtime)

When unsure what drivers exist in this dpdata version/env, query them at runtime:

import dpdata
from dpdata.driver import Driver

print(sorted(Driver.get_drivers().keys()))

import dpdata ensures built-in plugins are loaded before listing registered drivers.

In the current repo state, keys include:

• ase
• dp / deepmd / deepmd-kit
• gaussian
• sqm
• hybrid

(Exact set depends on dpdata version and installed extras.)

Minimal workflow

import dpdata
from dpdata.system import System

sys = System("input.xyz", fmt="xyz")
ls = sys.predict(driver="ase", calculator=...)  # returns dpdata.LabeledSystem

Verify you got a labeled system

assert "energies" in ls.data
# optional:
# assert "forces" in ls.data
# assert "virials" in ls.data

Example: use the ASE driver with an ASE calculator (runnable)

This is the easiest fully runnable example because it doesn’t require external QM software.

Dependencies (recommended): declare script dependencies with uv inline metadata, then run with uv run.

# /// script
# requires-python = ">=3.8"
# dependencies = [
#   "dpdata",
#   "numpy",
#   "ase",
# ]
# ///

Script:

from pathlib import Path

import numpy as np
from ase.calculators.lj import LennardJones
from dpdata.system import System

# write a tiny molecule
Path("tmp.xyz").write_text("""2\n\nH 0 0 0\nH 0 0 0.74\n""")

sys = System("tmp.xyz", fmt="xyz")
ls = sys.predict(driver="ase", calculator=LennardJones())

print("energies", np.array(ls.data["energies"]))
print("forces shape", np.array(ls.data["forces"]).shape)
if "virials" in ls.data:
    print("virials shape", np.array(ls.data["virials"]).shape)
else:
    print("virials: <not provided by this driver/calculator>")

Example: pass a Driver object instead of a string

from ase.calculators.lj import LennardJones
from dpdata.driver import Driver
from dpdata.system import System

sys = System("tmp.xyz", fmt="xyz")
ase_driver = Driver.get_driver("ase")(calculator=LennardJones())
ls = sys.predict(driver=ase_driver)

Hybrid driver

Use driver="hybrid" to sum energies/forces/virials from multiple drivers.

The HybridDriver accepts drivers=[ ... ] where each item is either:

• a Driver instance
• a dict like {"type": "sqm", ...} (type is the driver key)

Example (structure only; may require external executables):

from dpdata.driver import Driver

hyb = Driver.get_driver("hybrid")(
    drivers=[
        {"type": "sqm", "qm_theory": "DFTB3"},
        {"type": "dp", "dp": "frozen_model.pb"},
    ]
)
# ls = sys.predict(driver=hyb)

Notes / gotchas

• Many drivers require extra dependencies or external programs:
  • dp requires deepmd-kit + a model file
  • gaussian requires Gaussian and a valid executable (default g16)
  • sqm requires AmberTools sqm
• If you just need file format conversion, use the existing dpdata CLI skill instead.