Deepmd Python Inference

What it does

When your agent uses it

• The user wants to load a trained/frozen DeePMD model (.pth
• A built-in pretrained model (e.g.
• DPA-3.2-5M) in Python
• Predict energy/force/virial for atomic configurations

Example prompts

• “/deepmd-python-inference”

Requirements

• Python 3
• Compatibility (from SKILL.md): Requires deepmd-kit Python package installed. PyTorch backend for .pth models, TensorFlow for .pb models.

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

1. 1Determine the model source
2. 2Determine the inference task
3. 3Help the user prepare input arrays in the correct format.
4. 4Run inference and report results.

What it can do on your machine

Read from SKILL.md and the folder at commit 5c19e75. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are python and bash).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • docs.deepmodeling.com
  • github.com

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• Compatibility

  Requires deepmd-kit Python package installed. PyTorch backend for .pth models, TensorFlow for .pb models.

  From compatibility in the SKILL.md frontmatter.

Context cost

Deepmd Python Inference loads about 2.3k tokens when it runs. Until then it costs about 108 tokens; SKILL.md has 423 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~108

When it runs · the whole SKILL.md, loaded when a task matches

~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jinzhezenggroup/computational-chemistry-agent-skills at commit 5c19e75, republished under its LGPL-3.0-or-later licence (© jinzhezenggroup). 423 words, ~2,289 tokens.

name

deepmd-python-inference

description

Run Python inference with DeePMD-kit models using the DeepPot API. Use when the user wants to load a trained/frozen DeePMD model (.pth or .pb) or a built-in pretrained model (e.g., DPA-3.2-5M) in Python, predict energy/force/virial for atomic configurations, evaluate descriptors, or calculate model deviation between multiple models. Also covers using `dp test` CLI for batch evaluation against labeled data.

compatibility

Requires deepmd-kit Python package installed. PyTorch backend for .pth models, TensorFlow for .pb models.

license

LGPL-3.0-or-later

metadata.author

iProzd

metadata.version

1.0

metadata.repository

https://github.com/deepmodeling/deepmd-kit

from deepmd.infer import DeepPot
import numpy as np

dp = DeepPot("model.pth")
coord = np.array([[1, 0, 0], [0, 0, 1.5], [1, 0, 3]]).reshape([1, -1])
cell = np.diag(10 * np.ones(3)).reshape([1, -1])
atype = [1, 0, 1]
e, f, v = dp.eval(coord, cell, atype)

from deepmd.infer import DeepPot

# From a frozen PyTorch model
dp = DeepPot("model.pth")

# From a frozen TensorFlow model
dp = DeepPot("graph.pb")

# From a built-in pretrained model (auto-downloads if not cached)
dp = DeepPot("DPA-3.2-5M")

import numpy as np
from deepmd.infer import DeepPot

dp = DeepPot("model.pth")

# Prepare inputs
# coord: (nframes, natoms * 3) in Angstrom
# cell: (nframes, 9) cell vectors in Angstrom, row-major
# atype: list of atom type indices (length natoms)

coord = np.array(
    [
        [
            0.0,
            0.0,
            0.0,  # atom 0 (O)
            0.0,
            0.0,
            1.0,  # atom 1 (H)
            0.0,
            1.0,
            0.0,
        ]  # atom 2 (H)
    ]
).reshape([1, -1])

cell = np.diag([10.0, 10.0, 10.0]).reshape([1, -1])

# atype indices correspond to type_map order in the model
# e.g., if type_map = ["O", "H"], then O=0, H=1
atype = [0, 1, 1]

e, f, v = dp.eval(coord, cell, atype)

print(f"Energy (eV): {e}")  # shape: (nframes, 1)
print(f"Forces (eV/A): {f}")  # shape: (nframes, natoms, 3)
print(f"Virial (eV): {v}")  # shape: (nframes, 9)

e, f, v = dp.eval(coord, None, atype)

nframes = 10
natoms = 3

coords = np.random.rand(nframes, natoms * 3)
cells = np.tile(np.diag([10.0, 10.0, 10.0]).reshape([1, -1]), (nframes, 1))
atype = [0, 1, 1]

e, f, v = dp.eval(coords, cells, atype)
# e: (nframes, 1)
# f: (nframes, natoms, 3)
# v: (nframes, 9)

descriptors = dp.eval_descriptor(coord, cell, atype)
# shape: (nframes, natoms, ndesc)

dp eval-desc -m model.pth -s /path/to/system -o desc_output

from deepmd.infer import calc_model_devi, DeepPot

coord = np.array([[1, 0, 0], [0, 0, 1.5], [1, 0, 3]]).reshape([1, -1])
cell = np.diag(10 * np.ones(3)).reshape([1, -1])
atype = [1, 0, 1]

graphs = [DeepPot("model_0.pth"), DeepPot("model_1.pth")]
model_devi = calc_model_devi(coord, cell, atype, graphs)

# Basic test
dp --pt test -m model.pth -s /path/to/test_system -n 30

# Test with detailed output
dp --pt test -m model.pth -s /path/to/test_system -n 30 -d test_detail

Energy RMSE        : 1.234e-03 eV
Energy RMSE/Natoms : 6.427e-06 eV
Force  RMSE        : 2.345e-02 eV/A
Virial RMSE        : 5.678e-02 eV
Virial RMSE/Natoms : 2.957e-04 eV

import subprocess
import numpy as np
from deepmd.infer import DeepPot

# Step 1: Train (run in shell)
# dp --pt train input.json

# Step 2: Freeze (run in shell)
# dp --pt freeze -o model.pth

# Step 3: Python inference
dp = DeepPot("model.pth")

# Load test data from deepmd format
coord = np.load("test_system/set.000/coord.npy")  # (nframes, natoms*3)
cell = np.load("test_system/set.000/box.npy")  # (nframes, 9)
atype_raw = np.loadtxt("test_system/type.raw", dtype=int).tolist()

# Predict
e, f, v = dp.eval(coord, cell, atype_raw)

# Compare with reference
ref_energy = np.load("test_system/set.000/energy.npy")
ref_force = np.load("test_system/set.000/force.npy")

natoms = len(atype_raw)
energy_rmse = np.sqrt(np.mean((e.flatten() - ref_energy.flatten()) ** 2)) / natoms
force_rmse = np.sqrt(np.mean((f.reshape(-1) - ref_force.reshape(-1)) ** 2))

print(f"Energy RMSE/atom: {energy_rmse:.6f} eV")
print(f"Force RMSE:       {force_rmse:.6f} eV/A")

from deepmd.infer import DeepPot
import numpy as np

# Auto-downloads DPA-3.2-5M on first use
dp = DeepPot("DPA-3.2-5M")

# Water molecule example
coord = np.array(
    [
        [0.000, 0.000, 0.117],  # O
        [0.000, 0.757, -0.469],  # H
        [0.000, -0.757, -0.469],  # H
    ]
).reshape([1, -1])

cell = np.diag([10.0, 10.0, 10.0]).reshape([1, -1])
atype = [0, 1, 1]  # Check model's type_map for correct indices

e, f, v = dp.eval(coord, cell, atype)
print(f"Energy: {e[0][0]:.6f} eV")
print(f"Forces:\n{f[0]}")

dp pretrained download DPA-3.3-1M
dp pretrained download DPA-3.2-5M
dp pretrained download DPA-3.1-3M
dp pretrained download DPA-3.2-5M --cache-dir ./models

© jinzhezenggroup, LGPL-3.0-or-later. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in machine-learning-potentials/deepmd-python-inference of jinzhezenggroup/computational-chemistry-agent-skills.

Open the folder on GitHubat commit 5c19e75