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Agent skill

Mat Sample Pes By Md

by learningmatter-mit in learningmatter-mit/AtomisticSkills

Sample off-equilibrium potential energy surface (PES), used for benchmarking and fine-tuning MLIPs.

MITAuto-check passedAI & LLM Engineering

Install Mat Sample Pes By Md

skills CLI
$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-sample-pes-by-md -a claude-code

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

GitHub CLI
$ gh skill install learningmatter-mit/AtomisticSkills mat-sample-pes-by-md --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/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/mat-sample-pes-by-md .claude/skills/mat-sample-pes-by-md && 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
mat-sample-pes-by-md
GitHub stars
175
Token cost
~850 tokens
SKILL.md length
262 words
Files
8 (incl. scripts)
Skills in repo
129
Repo updated
First seen
Licence
MIT

At a glance

Sample off-equilibrium potential energy surface (PES), used for benchmarking and fine-tuning MLIPs.

  • Works in 2 steps: Prepare a Foundation Potential: Select… → Off-Equilibrium Sampling (MD-Clustering)
  • Tasks that involve Fine-tuning
  • SKILL.md covers Goal, Instructions, Standalone Usage (Python API) and Examples, plus 1 more section
  • Runs Python scripts from its folder

What it does

Mat Sample Pes By Md is an agent skill from learningmatter-mit/AtomisticSkills. Sample off-equilibrium potential energy surface (PES), used for benchmarking and fine-tuning MLIPs.

Its SKILL.md is about 850 tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts (for example `examples/LiMnO2/README.md`, `scripts/feature_calculators.py` and `scripts/run_sampling.py`).

It sits in AI & LLM Engineering, covering Fine-tuning. The repository describes itself as: Integrating AtomisticSkills into Agentic IDEs (Cursor, Claude Code, Codex, Google Antigravity, Hermes Agent, etc). The licence is MIT.

When your agent uses it

  • Tasks that involve Fine-tuning

Example prompts

  • “/mat-sample-pes-by-md”

Requirements

  • Python 3

Workflow steps

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

  1. Prepare a Foundation Potential: Select an appropriate MLIP model for sampling.
  2. Off-Equilibrium Sampling (MD-Clustering)

What it can do on your machine

Read from SKILL.md and the folder at commit 7f2d86d. 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

    Ships 3 files in scripts/ (Python), which the agent can run.

    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):

    • 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.

Context cost

Mat Sample Pes By Md loads about 850 tokens when it runs. Until then it costs about 30 tokens; SKILL.md has 262 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~30
When it runs · the whole SKILL.md, loaded when a task matches
~850

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); the scripts in this folder are not scanned.

SKILL.md

The full file from learningmatter-mit/AtomisticSkills at commit 7f2d86d, republished under its MIT licence (© learningmatter-mit). 262 words, ~850 tokens.

Download SKILL.mdSave it as .claude/skills/mat-sample-pes-by-md/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
mat-sample-pes-by-md
description
Sample off-equilibrium potential energy surface (PES), used for benchmarking and fine-tuning MLIPs.
metadata.category
materials, chemistry, machine-learning
metadata.venv
mlip

Sample PES by MD

Goal

To generate diverse and representative atomic configurations from a starting structure to augment training data for Machine Learning Interatomic Potentials (MLIPs). This is achieved through MD-based sampling with crystal feature clustering.

Instructions

  1. Prepare a Foundation Potential: Select an appropriate MLIP model for sampling.

    • Recommended: M3GNet-PES-MatPES-PBE-2025.2 (MatGL) or MACE-MP-small (MACE) for general inorganic materials.

  2. Off-Equilibrium Sampling (MD-Clustering):

    • Use the unified sampling script to run a short MD trajectory and pick representative configurations via K-Means clustering of latent features.

    Using MatGL (CHGNet):

    bash
    ${CLAUDE_SKILL_DIR}/../../venv/run mlip python ${CLAUDE_SKILL_DIR}/scripts/run_sampling.py input.cif \
    --model_type matgl --model_name CHGNet-PES-MatPES-PBE-1M-2026.9 \
    --total_steps 2000 --temperature 1000 --n_clusters 10 --output_dir sampling_results

    Using MACE:

    bash
    ${CLAUDE_SKILL_DIR}/../../venv/run mlip python ${CLAUDE_SKILL_DIR}/scripts/run_sampling.py input.cif \
    --model_type mace --model_name MACE-OMAT-0-small \
    --total_steps 2000 --temperature 1000 --n_clusters 10 --output_dir sampling_results
Supercell Expansion

The script automatically expands small cells (e.g., primitive cells) to supercells containing ~50 atoms (close-to-cubic) before simulation. This ensures adequate system size and local environment diversity.

  • Customize: Set --target_atoms in the script call (recommended: 40-70 atoms for VASP efficiency).
  • Limit: Maximum atoms capped at 120 to prevent OOM in subsequent DFT calculations.

Standalone Usage (Python API)

For integration into other Python workflows, use the OffEquilibriumSampler class directly.

python
from .agents.skills.mat_sample_pes_by_md.scripts.sampler import OffEquilibriumSampler
from .agents.skills.mat_sample_pes_by_md.scripts.feature_calculators import MatGLCrystalFeatureCalculator
from matgl import load_model

# Setup calculator
model = load_model("M3GNet-PES-MatPES-PBE-2025.2")
calc = MatGLCrystalFeatureCalculator(potential=model)

# Initialize and run sampler
sampler = OffEquilibriumSampler(
    calculator=calc,
    atoms=initial_atoms,
    total_steps=1000,
    temperature=800,
    n_clusters=20
)
structures, metadata = sampler.sample()

Examples

High-Temperature Sampling of LiMnO2 (MatGL-CHGNet)

Sampling 10 representative configurations from a 10 ps MD trajectory of LiMnO2 at 2000K.

Constraints

  • Environments:
    • Off-Equilibrium (MatGL): Requires the mlip environment.
    • Off-Equilibrium (MACE): Requires the mlip environment.
  • Time Step: Automatically set to 5.0 fs for stability, or 2.0 fs if Hydrogen is detected.
  • Clustering: Requires scikit-learn in the environment.

Author: Bowen Deng Contact: GitHub @learningmatter-mit

© learningmatter-mit, MIT. 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 7 other files (scripts) in skills/mat-sample-pes-by-md of learningmatter-mit/AtomisticSkills.

  • SKILL.md
  • examples/LiMnO2/LiMnO2_MatGL_0.60ps.cif
  • examples/LiMnO2/LiMnO2_MatGL_8.05ps.cif
  • examples/LiMnO2/LiMnO2_MatGL_9.45ps.cif
  • examples/LiMnO2/README.md
  • scripts/feature_calculators.py
  • scripts/run_sampling.py
  • scripts/sampler.py

Open the folder on GitHubat commit 7f2d86d

Compare with similar skills

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Categories

Questions about Mat Sample Pes By Md

What does Mat Sample Pes By Md do?

Sample off-equilibrium potential energy surface (PES), used for benchmarking and fine-tuning MLIPs. Mat Sample Pes By Md is an agent skill from learningmatter-mit/AtomisticSkills. Sample off-equilibrium potential energy surface (PES), used for benchmarking and fine-tuning MLIPs.

When should I use Mat Sample Pes By Md?

Mat Sample Pes By Md fits situations like: tasks that involve Fine-tuning.

How do I install Mat Sample Pes By Md in Claude Code?

Run `npx skills add learningmatter-mit/AtomisticSkills --skill mat-sample-pes-by-md -a claude-code`. Or copy the skill folder (skills/mat-sample-pes-by-md in learningmatter-mit/AtomisticSkills) into .claude/skills/mat-sample-pes-by-md in your project. Claude Code loads it when a task matches its description.

How do I install Mat Sample Pes By Md in Codex?

Run `npx skills add learningmatter-mit/AtomisticSkills --skill mat-sample-pes-by-md -a codex`. Or copy the skill folder (skills/mat-sample-pes-by-md in learningmatter-mit/AtomisticSkills) into .agents/skills/mat-sample-pes-by-md in your project. Codex loads it when a task matches its description.

Can I use Mat Sample Pes By Md 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 learningmatter-mit/AtomisticSkills --skill mat-sample-pes-by-md -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mat-sample-pes-by-md, .gemini/skills/mat-sample-pes-by-md, .github/skills/mat-sample-pes-by-md and .opencode/skills/mat-sample-pes-by-md in your project.

What does Mat Sample Pes By Md need to run?

Going by SKILL.md and its folder, Mat Sample Pes By Md needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Mat Sample Pes By Md access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Mat Sample Pes By Md 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Mat Sample Pes By Md use?

Mat Sample Pes By Md is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Mat Sample Pes By Md use?

About 850 tokens (SKILL.md is roughly 3.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Mat Sample Pes By Md?

Skills that share tags, products or a category with Mat Sample Pes By Md: Peft Fine Tuning (Orchestra-Research/AI-Research-SKILLs, 13k stars), Hugging Face LLM Trainer (huggingface/skills, 11k stars), Sentence-Transformers Training Router (huggingface/skills, 11k stars) and Dataset Evaluation (awslabs/agent-plugins, 912 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mat Sample Pes By Md?

learningmatter-mit (a GitHub organization) maintains it in learningmatter-mit/AtomisticSkills, which has 175 GitHub stars. The repository holds 129 skills in this directory. The repository was last updated on October 6, 2026.

Source: learningmatter-mit/AtomisticSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.