Prepares and runs molecular dynamics simulations in LAMMPS with a DeePMD machine-learning potential, writing the input script and choosing NVE, NVT or NPT.

LGPL-3.0-or-laterAuto-check passedResearch & Science

Install LAMMPS with DeePMD-kit

skills CLI
$ npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill lammps-deepmd -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jinzhezenggroup/computational-chemistry-agent-skills lammps-deepmd --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jinzhezenggroup/computational-chemistry-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/molecular-dynamics/lammps-deepmd .claude/skills/lammps-deepmd && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
lammps-deepmd
GitHub stars
148
Token cost
~2.8k tokens
SKILL.md length
1,257 words
Files
3 (incl. references, assets)
Skills in repo
62
Repo updated
First seen
Licence
LGPL-3.0-or-later

At a glance

Prepares and runs molecular dynamics simulations in LAMMPS with a DeePMD machine-learning potential, writing the input script and choosing NVE, NVT or NPT.

  • Works in 6 steps: Confirm the available execution mode → Confirm the minimum simulation inputs → Write the LAMMPS input script yourself… → …
  • Setting up a LAMMPS run that uses a DeePMD potential
  • SKILL.md covers Agent responsibilities, Decide the execution mode, Minimal information to collect and Recommended workflow, plus 5 more sections
  • Calls uvx

What it does

The skill guides your agent through setting up a LAMMPS run that uses a DeePMD-kit model such as graph.pb. It first settles how LAMMPS will be run: online through uvx when internet access and uv are available, or offline with an executable, module or container that you name, since the agent is told never to invent one. It then collects the structure file, the model file, the atom-type-to-element mapping with masses, the ensemble, temperature, pressure, timestep and number of steps.

The agent writes input.lammps itself and explains every command in it. The skill ships an example NVT input and a reference on commands and workflow. Where possible it checks command availability against the LAMMPS docs or lmp -h, and it reports which command ran, which files it used and where the outputs went. The first online run can be slow while the packages are provisioned.

When your agent uses it

  • Setting up a LAMMPS run that uses a DeePMD potential
  • Explaining what each command in an input.lammps file does
  • Switching a simulation between NVE, NVT and NPT ensembles
  • Running a DeePMD-enabled LAMMPS job from a cluster module or container

Example prompts

  • “Write an NVT input script for data.system using graph.pb and walk me through every command.”
  • “Convert my NVT LAMMPS input to NPT and tell me what changed.”
  • “Run the simulation with uvx and report where the output files were written.”
  • “Our cluster only has a LAMMPS module. Help me run the DeePMD job with it.”

Requirements

  • LAMMPS built with DeePMD-kit support
  • uv for online mode, or a LAMMPS executable, module or container for offline mode
  • A DeePMD model file such as graph.pb and a structure data file
  • Compatibility (from SKILL.md): Requires LAMMPS with DeePMD-kit support. Online mode prefers `uvx --from lammps --with deepmd-kit[gpu,torch,lmp] lmp`; offline mode requires a user-provided LAMMPS executable or module.

Workflow steps

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

  1. Confirm the available execution mode
  2. Confirm the minimum simulation inputs
  3. Write the LAMMPS input script yourself instead of asking the user to hand-write it.
  4. Keep the example readable and fully explained. If you include an example input script, explain what every command does.
  5. When possible, validate command availability against the LAMMPS docs or local lmp -h output before execution.
  6. Report clearly which command was run, which files were used, and where outputs were written.

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

    Shell commands in SKILL.md call:

    • uvx

    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.lammps.org
    • 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 LAMMPS with DeePMD-kit support. Online mode prefers `uvx --from lammps --with deepmd-kit[gpu,torch,lmp] lmp`; offline mode requires a user-provided LAMMPS executable or module.

    From compatibility in the SKILL.md frontmatter.

Context cost

LAMMPS with DeePMD-kit loads about 2.8k tokens when it runs, and up to ~3.3k if it reads all its reference files. Until then it costs about 100 tokens; SKILL.md has 1,257 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~100
When it runs · the whole SKILL.md, loaded when a task matches
~2.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.3k

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

Auto-check passed

The automated check found no risky patterns in SKILL.md.

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). 1,257 words, ~2,832 tokens.

Download SKILL.mdSave it as .claude/skills/lammps-deepmd/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
lammps-deepmd
description
A tool and knowledge base for running molecular dynamics (MD) simulations in LAMMPS with the DeePMD-kit plugin. It handles input script preparation, ensemble selection (NVE/NVT/NPT), and job execution via `uv` or offline binaries. USE WHEN you need to set up, write, explain, or execute a LAMMPS molecular dynamics simulation using a DeePMD machine learning potential (e.g., `graph.pb`).
compatibility
Requires LAMMPS with DeePMD-kit support. Online mode prefers `uvx --from lammps --with deepmd-kit[gpu,torch,lmp] lmp`; offline mode requires a user-provided LAMMPS executable or module.
license
LGPL-3.0-or-later
metadata.author
OpenClaw
metadata.version
1.0
metadata.repository
https://github.com/deepmodeling/deepmd-kit
metadata.lammps_docs
https://docs.lammps.org/

LAMMPS + DeePMD-kit

Use this skill when the user wants to run molecular dynamics in LAMMPS with a DeePMD-kit potential, prepare or explain an input.lammps file, or switch between common ensembles such as NVE, NVT, and NPT.

Agent responsibilities

  1. Confirm the available execution mode:
    • Online mode: if internet access is available and uv is installed, prefer uvx --from lammps --with deepmd-kit[gpu,torch,lmp] lmp ...
    • Offline mode: do not guess the executable. Ask the user which LAMMPS command, module, or container should be used.
  2. Confirm the minimum simulation inputs:
    • structure/data file (for example data.system)
    • DeePMD model file (for example graph.pb or compressed model)
    • atom type to element mapping, including required per-type masses if the data file does not define them
    • target ensemble (NVE, NVT, NPT, or another explicitly requested setup)
    • temperature, pressure if applicable, timestep, and total number of steps
  3. Write the LAMMPS input script yourself instead of asking the user to hand-write it.
  4. Keep the example readable and fully explained. If you include an example input script, explain what every command does.
  5. When possible, validate command availability against the LAMMPS docs or local lmp -h output before execution.
  6. Report clearly which command was run, which files were used, and where outputs were written.

Decide the execution mode

Online mode (preferred when internet access is available)

Use:

bash
uvx --from lammps --with deepmd-kit[gpu,torch,lmp] lmp -in input.lammps

If you need to inspect the local command-line help:

bash
uvx --from lammps --with deepmd-kit[gpu,torch,lmp] lmp -h | tee /dev/tty

Notes:

  • This is the preferred path because it can provision LAMMPS and DeePMD-kit on demand.
  • The gpu,torch,lmp extras match the requested runtime pattern from the user.
  • If the environment is slow or the packages are large, warn the user that the first run may take time.
Offline mode

If internet access is unavailable or the user explicitly wants a site-installed binary, ask a concrete question such as:

  • "Which LAMMPS executable should I use, for example lmp, lmp_mpi, mpirun -np 8 lmp, or an HPC module command?"
  • "Do you already have a DeePMD-enabled LAMMPS build on this machine or cluster?"

Do not invent a binary name or module name.

Minimal information to collect

Ask only for what is missing:

  • DeePMD model path
  • LAMMPS data file path
  • ensemble
  • target temperature
  • target pressure if using NPT
  • timestep
  • run length in steps
  • whether velocities should be generated from scratch
  • preferred execution command if offline
  1. Inspect available files in the working directory.
  2. Draft input.lammps.
  3. Explain the script to the user if they asked for an explanation or if the script is nontrivial.
  4. Run a short smoke test first when reasonable.
  5. Run the full simulation.
  6. Summarize outputs such as log.lammps, dump trajectories, restart files, and thermodynamic data.

Example: annotated NVT input

The following example is adapted from the user-provided tutorial pattern and slightly generalized. See also assets/input.nvt.lammps.

lammps
variable        NSTEPS          equal 1000000
variable        THERMO_FREQ     equal 1000
variable        DUMP_FREQ       equal 1000
variable        TEMP            equal 300.0
variable        TAU_T           equal 0.1

units           metal
boundary        p p p
atom_style      atomic

neighbor        1.0 bin

read_data       data.system
mass            1 28.0855
mass            2 15.999
pair_style      deepmd graph_compressed.pb
pair_coeff      * *

thermo_style    custom step temp pe ke etotal press vol lx ly lz xy xz yz
thermo          ${THERMO_FREQ}
dump            1 all custom ${DUMP_FREQ} traj.lammpstrj id type x y z

velocity        all create ${TEMP} 743574
fix             1 all nvt temp ${TEMP} ${TEMP} ${TAU_T}

timestep        0.0005
run             ${NSTEPS}
What every command means
  • variable NSTEPS equal 1000000

    • Defines a numeric variable called NSTEPS with value 1000000.
    • Used later by run ${NSTEPS} so the run length is easy to modify in one place.
  • variable THERMO_FREQ equal 1000

    • Defines how often LAMMPS prints thermodynamic information.
    • Used by thermo ${THERMO_FREQ}.
  • variable DUMP_FREQ equal 1000

    • Defines how often coordinates are written to the trajectory dump.
  • variable TEMP equal 300.0

    • Sets the target temperature in the current unit system.
    • Because units metal is used below, this temperature is interpreted in kelvin.
  • variable TAU_T equal 0.1

    • Sets the thermostat damping parameter used by the NVT fix.
    • In metal units this is in picoseconds.
  • units metal

    • Selects the LAMMPS metal unit system.
    • This determines the physical meaning of timestep, temperature, pressure, energy, distance, and time.
    • In this unit system, distances are in angstrom, time is in picoseconds, and the timestep should be chosen accordingly.
  • boundary p p p

    • Applies periodic boundary conditions in x, y, and z.
    • Suitable for bulk condensed-phase simulations.
  • atom_style atomic

    • Uses the atomic atom style, appropriate when atoms have no explicit bonds, angles, or molecular topology in the force field description.
    • Common for DeePMD simulations of condensed phases when the structure is provided as atoms in a box.
  • neighbor 1.0 bin

    • Sets the neighbor-list skin distance to 1.0 in the current distance unit.
    • Uses the bin neighbor-building method.
    • Neighbor lists help LAMMPS efficiently find nearby atoms for force evaluation.
  • read_data data.system

    • Reads the initial atomic structure, atom types, simulation box, and related information from the LAMMPS data file data.system.
    • Replace this filename with the actual user file.
  • mass 1 28.0855, mass 2 15.999

    • Defines per-type atomic masses when the data file does not contain a Masses section.
    • These example values correspond to a two-type Si/O mapping; adjust them to the actual atom type to element mapping. LAMMPS velocity creation and thermostats require masses; without them, runs can fail with Not all per-type masses are set.
  • pair_style deepmd graph_compressed.pb

    • Selects the DeePMD pair style.
    • Loads the DeePMD model from graph_compressed.pb.
    • Replace the model filename with the actual model path, for example graph.pb, graph-compress.pb, or another supported exported model.
  • pair_coeff * *

    • Activates the previously selected pair style for all atom types.
    • For DeePMD this often takes the simple form * * because the mapping is embedded in the model workflow rather than through conventional pairwise parameters.
  • thermo_style custom step temp pe ke etotal press vol lx ly lz xy xz yz

    • Chooses exactly which thermodynamic quantities to print.
    • step: timestep index.
    • temp: instantaneous temperature.
    • pe: potential energy.
    • ke: kinetic energy.
    • etotal: total energy.
    • press: pressure.
    • vol: box volume.
    • lx ly lz: box lengths.
    • xy xz yz: triclinic tilt factors, which are harmless to print even for an orthogonal box.
  • thermo ${THERMO_FREQ}

    • Prints the thermo block every THERMO_FREQ timesteps.
  • dump 1 all custom ${DUMP_FREQ} traj.lammpstrj id type x y z

    • Creates dump ID 1.
    • Dumps atoms from group all.
    • Uses the custom dump format.
    • Writes every DUMP_FREQ steps.
    • Saves to traj.lammpstrj.
    • Outputs per-atom columns id type x y z.
  • velocity all create ${TEMP} 743574

    • Assigns random initial velocities to all atoms.
    • The target temperature is TEMP.
    • 743574 is the random seed.
    • Use this when starting a fresh MD trajectory. If restarting from a previous equilibrated state, this command may be unnecessary.
  • fix 1 all nvt temp ${TEMP} ${TEMP} ${TAU_T}

    • Creates fix ID 1 on group all.
    • Applies the Nose-Hoover NVT thermostat.
    • The target temperature is ramped from ${TEMP} to ${TEMP}, meaning constant temperature here.
    • ${TAU_T} is the thermostat damping constant.
  • timestep 0.0005

    • Sets the MD timestep.
    • In metal units, 0.0005 means 0.0005 ps = 0.5 fs.
    • The safe choice depends on the system and model quality.
  • run ${NSTEPS}

    • Runs molecular dynamics for NSTEPS timesteps.
Show full SKILL.md (185 more words)Show less

Common ensemble modifications

NVE

Replace the NVT thermostat line with:

lammps
fix 1 all nve

Meaning:

  • integrates Newton's equations in the microcanonical ensemble
  • no thermostat or barostat is applied
  • useful for short stability checks or production runs after equilibration
NPT

A typical isotropic NPT alternative is:

lammps
variable        PRESS           equal 1.0
variable        TAU_P           equal 1.0
fix             1 all npt temp ${TEMP} ${TEMP} ${TAU_T} iso ${PRESS} ${PRESS} ${TAU_P}

Meaning:

  • PRESS is the target pressure
  • TAU_P is the barostat damping constant
  • iso applies isotropic pressure control to the simulation box
  • this simultaneously thermostats and barostats the system

When using NPT, it is often useful to keep vol, lx, ly, and lz in the thermo output.

Execution templates

Online run
bash
uvx --from lammps --with deepmd-kit[gpu,torch,lmp] lmp -in input.lammps
Online help
bash
uvx --from lammps --with deepmd-kit[gpu,torch,lmp] lmp -h | tee /dev/tty
Offline run

Only after the user specifies the executable, use a command such as one of these exact patterns:

bash
lmp -in input.lammps
mpirun -np 8 lmp_mpi -in input.lammps
srun lmp -in input.lammps

The agent must not choose one of these on its own without user guidance in offline mode.

Output checklist

After a run, report at least:

  • executed command
  • input script path
  • data file path
  • model path
  • main log path
  • trajectory path if any
  • whether the run completed successfully
  • any obvious warnings or errors from the log

References

© 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

SKILL.md and 2 other files (references, assets) in molecular-dynamics/lammps-deepmd of jinzhezenggroup/computational-chemistry-agent-skills.

  • SKILL.md
  • assets/input.nvt.lammps
  • references/commands-and-workflow.md

Open the folder on GitHubat commit 5c19e75

Compare with similar skills

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Questions about LAMMPS with DeePMD-kit

What does LAMMPS with DeePMD-kit do?

Prepares and runs molecular dynamics simulations in LAMMPS with a DeePMD machine-learning potential, writing the input script and choosing NVE, NVT or NPT. pb. It first settles how LAMMPS will be run: online through uvx when internet access and uv are available, or offline with an executable, module or container that you name, since the agent is told never to invent one.

When should I use LAMMPS with DeePMD-kit?

LAMMPS with DeePMD-kit fits situations like: setting up a LAMMPS run that uses a DeePMD potential; explaining what each command in an input.lammps file does; switching a simulation between NVE, NVT and NPT ensembles; running a DeePMD-enabled LAMMPS job from a cluster module or container.

How do I install LAMMPS with DeePMD-kit in Claude Code?

Run `npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill lammps-deepmd -a claude-code`. Or copy the skill folder (molecular-dynamics/lammps-deepmd in jinzhezenggroup/computational-chemistry-agent-skills) into .claude/skills/lammps-deepmd in your project. Claude Code loads it when a task matches its description.

How do I install LAMMPS with DeePMD-kit in Codex?

Run `npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill lammps-deepmd -a codex`. Or copy the skill folder (molecular-dynamics/lammps-deepmd in jinzhezenggroup/computational-chemistry-agent-skills) into .agents/skills/lammps-deepmd in your project. Codex loads it when a task matches its description.

Can I use LAMMPS with DeePMD-kit in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill lammps-deepmd -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/lammps-deepmd, .gemini/skills/lammps-deepmd, .github/skills/lammps-deepmd and .opencode/skills/lammps-deepmd in your project.

What does LAMMPS with DeePMD-kit need to run?

Going by SKILL.md and its folder, LAMMPS with DeePMD-kit needs the command-line tools its instructions call (uvx). Our summary lists: LAMMPS built with DeePMD-kit support; uv for online mode, or a LAMMPS executable, module or container for offline mode; A DeePMD model file such as graph.pb and a structure data file. Compatibility (from SKILL.md): Requires LAMMPS with DeePMD-kit support. Online mode prefers `uvx --from lammps --with deepmd-kit[gpu,torch,lmp] lmp`; offline mode requires a user-provided LAMMPS executable or module..

Does LAMMPS with DeePMD-kit access the network?

SKILL.md names 2 domains. As links in the text: docs.lammps.org and github.com. This is read from the text; nothing was executed.

Is LAMMPS with DeePMD-kit safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does LAMMPS with DeePMD-kit use?

LAMMPS with DeePMD-kit is published under the LGPL-3.0-or-later licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does LAMMPS with DeePMD-kit use?

About 2.8k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 474 tokens, read only when the agent opens those files.

What are the alternatives to LAMMPS with DeePMD-kit?

Skills that share tags, products or a category with LAMMPS with DeePMD-kit: Journal Of Climate (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars), Lammps Deepmd (Hello-QM/catgo-LRG, 205 stars), Astropy (zLanqing/codex-claude-academic-skills, 4.7k stars) and Pymatgen (zLanqing/codex-claude-academic-skills, 4.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains LAMMPS with DeePMD-kit?

jinzhezenggroup (a GitHub organization) maintains it in jinzhezenggroup/computational-chemistry-agent-skills, which has 148 GitHub stars. The repository holds 62 skills in this directory. The repository was last updated on October 9, 2026.

Source: jinzhezenggroup/computational-chemistry-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.