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Agent skill

Mat Qha Thermal Expansion

by learningmatter-mit in learningmatter-mit/AtomisticSkills

Calculate Quasi-Harmonic Approximation (QHA) thermal properties using MLIPs.

MITAuto-check passed

Install Mat Qha Thermal Expansion

skills CLI
$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-qha-thermal-expansion -a claude-code

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

GitHub CLI
$ gh skill install learningmatter-mit/AtomisticSkills mat-qha-thermal-expansion --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-qha-thermal-expansion .claude/skills/mat-qha-thermal-expansion && 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-qha-thermal-expansion
GitHub stars
176
Token cost
~952 tokens
SKILL.md length
444 words
Files
6 (incl. scripts)
Skills in repo
129
Repo updated
First seen
Licence
MIT

At a glance

Calculate Quasi-Harmonic Approximation (QHA) thermal properties using MLIPs.

  • Works in 6 steps: Prerequisites → Choosing a Foundation Potential → Choosing the Volume Window → …
  • SKILL.md covers 1. Prerequisites, 2. Choosing a Foundation…, 3. Choosing the Volume Window and 4. Calculation Workflow, plus 2 more sections
  • Runs Python scripts from its folder

What it does

Mat Qha Thermal Expansion is an agent skill from learningmatter-mit/AtomisticSkills. Calculate Quasi-Harmonic Approximation (QHA) thermal properties using MLIPs.

Its SKILL.md is about 950 tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts (for example `examples/Li_BCC_TensorNet/README.md`, `examples/Li_BCC_TensorNet/qha_results.json` and `scripts/calculate_qha.py`).

The repository describes itself as: Integrating AtomisticSkills into Agentic IDEs (Cursor, Claude Code, Codex, Google Antigravity, Hermes Agent, etc). The licence is MIT.

Example prompts

  • “/mat-qha-thermal-expansion”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Prerequisites
  2. Choosing a Foundation Potential
  3. Choosing the Volume Window
  4. Calculation Workflow
  5. Output Files
  6. Examples

What it can do on your machine

Read from SKILL.md and the folder at commit 6257444. 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 1 file 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 Qha Thermal Expansion loads about 952 tokens when it runs. Until then it costs about 26 tokens; SKILL.md has 444 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~26
When it runs · the whole SKILL.md, loaded when a task matches
~952

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 6257444, republished under its MIT licence (© learningmatter-mit). 444 words, ~952 tokens.

Download SKILL.mdSave it as .claude/skills/mat-qha-thermal-expansion/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
mat-qha-thermal-expansion
description
Calculate Quasi-Harmonic Approximation (QHA) thermal properties using MLIPs.
metadata.category
materials
metadata.venv
mlip

QHA Thermal Expansion Skill

This skill provides tools for calculating thermal expansion and temperature-dependent Gibbs energy using Machine Learning Interatomic Potentials (MLIPs).

1. Prerequisites

  • The appropriate MLIP wrapper must be available (MACEWrapper, MatGLWrapper, or FAIRCHEMWrapper).
  • matcalc is included in the mlip and fairchem environments.

2. Choosing a Foundation Potential

QHA calculations require accurate lattice expansion and vibrational properties.

[!IMPORTANT]

  • Use OMAT or MatPES trained models: These models (e.g., MACE-OMAT-0-small, TensorNet-MatPES-r2SCAN) are specifically optimized for forces and vibrational stability.
  • Avoid MPtrj-trained models: Models trained primarily on the MPtrj dataset (e.g., CHGNet-MPtrj) suffer from the "softening" problem, where the calculated phonon frequencies are significantly lower than DFT values.

Refer to the foundation-potentials skill for more details.

3. Choosing the Volume Window

QHA fits the free energy against volume -- phonopy-qha fits E(V) + F_vib(V,T) to a Vinet, Birch-Murnaghan or Murnaghan equation of state at each temperature and minimises it -- so the sampled volume range is a real input, and you should report it alongside the result.

The convention is +/-5% in LINEAR strain, which is -14% to +16% in volume:

sourcewindowvolume width
matcalc QHACalc default scale_factors0.95-1.05 linear1.35x
atomate2 QhaMaker default linear_strain(-0.05, 0.05)1.35x
phonopy Si-QHA example e-v.dat140.03-189.07 A^31.35x

Note the cube: a window quoted as "+/-5%" in lattice parameter is three times that in volume. Read which convention a tool means before comparing windows across codes -- QHACalc scales the lattice (apply_strain), not the volume.

[!IMPORTANT] The window must bracket the free-energy minimum at your highest temperature. The lattice expands on heating, so a window adequate at 0 K can be too narrow at high T, and a minimiser that runs into the edge of the scan returns the edge rather than the minimum. Check that the equilibrium volume at your top temperature is interior to the sampled volumes, and widen --volume_window if it is not. phonopy requires at least 5 volume points; 11 is the usual choice.

Show full SKILL.md (124 more words)Show less

[!NOTE] Widening the window is not automatically safer. On BCC lithium with M3GNet-PES-MatPES-PBE, going from +/-10% to the conventional +/-14/+16% in volume moves the 0 K equilibrium volume by 0.09 A^3 (0.5%) and the thermal expansion coefficient by 9%, with every sampled volume still dynamically stable -- so this is fit sensitivity, not a soft-mode artefact. Neither window is wrong; quote the one you used. If you need a number comparable to someone else's, match their window rather than assuming a default agrees.

4. Calculation Workflow

To calculate thermal expansion and temperature-dependent Gibbs energy, use calculate_qha.py.

bash
${CLAUDE_SKILL_DIR}/../../venv/run mlip python ${CLAUDE_SKILL_DIR}/scripts/calculate_qha.py \
    --structure path/to/relaxed_structure.cif \
    --model_type matgl \
    --eos vinet \
    --output_dir research/my_folder/qha

5. Output Files

  • qha_results.json: Summary.
  • gibbs_temperature.dat: Gibbs energy vs T.
  • thermal_expansion.dat: Thermal expansion vs T.

6. Examples

See examples/ for detailed usage scenarios.

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 5 other files (scripts) in skills/mat-qha-thermal-expansion of learningmatter-mit/AtomisticSkills.

  • SKILL.md
  • examples/Li_BCC_TensorNet/README.md
  • examples/Li_BCC_TensorNet/gibbs_temperature.dat
  • examples/Li_BCC_TensorNet/qha_results.json
  • examples/Li_BCC_TensorNet/thermal_expansion.dat
  • scripts/calculate_qha.py

Open the folder on GitHubat commit 6257444

Compare with similar skills

Mat Qha Thermal Expansion next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Eol Resistor Calculatorsickn33/agentic-awesome-skills47k1 repos~3.5kAutomated safety check: PassMIT
Text Expansionthedaviddias/Front-End-Checklist74k—~534Automated safety check: PassMIT

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Questions about Mat Qha Thermal Expansion

What does Mat Qha Thermal Expansion do?

Calculate Quasi-Harmonic Approximation (QHA) thermal properties using MLIPs. Mat Qha Thermal Expansion is an agent skill from learningmatter-mit/AtomisticSkills. Calculate Quasi-Harmonic Approximation (QHA) thermal properties using MLIPs.

How do I install Mat Qha Thermal Expansion in Claude Code?

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

How do I install Mat Qha Thermal Expansion in Codex?

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

Can I use Mat Qha Thermal Expansion 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-qha-thermal-expansion -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-qha-thermal-expansion, .gemini/skills/mat-qha-thermal-expansion, .github/skills/mat-qha-thermal-expansion and .opencode/skills/mat-qha-thermal-expansion in your project.

What does Mat Qha Thermal Expansion need to run?

Going by SKILL.md and its folder, Mat Qha Thermal Expansion needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Mat Qha Thermal Expansion 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 Qha Thermal Expansion 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 Qha Thermal Expansion use?

Mat Qha Thermal Expansion 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 Qha Thermal Expansion use?

About 952 tokens (SKILL.md is roughly 3.8k 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 Qha Thermal Expansion?

Skills that share tags, products or a category with Mat Qha Thermal Expansion: Quasi Coder (github/awesome-copilot, 40k stars), Intl Expansion (alirezarezvani/claude-skills, 28k stars), Harmonic Pattern Signals (HKUDS/Vibe-Trading, 35k stars) and Eol Resistor Calculator (sickn33/agentic-awesome-skills, 47k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mat Qha Thermal Expansion?

learningmatter-mit (a GitHub organization) maintains it in learningmatter-mit/AtomisticSkills, which has 176 GitHub stars. The repository holds 129 skills in this directory. The repository was last updated on October 7, 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.