mat-qha-thermal-expansion

mat-qha-thermal-expansion is a skill for Claude Code, Codex from learningmatter-mit/AtomisticSkills. It costs 26 tokens per session (992 once invoked), scanned A, original, MIT.

A calculation method for predicting how a material’s size, vibrational behaviour, and Gibbs energy change with temperature. It uses machine-learning models of atomic forces and the quasi-harmonic approximation, which estimates temperature effects from vibrations around changing volumes.

In plain words
What is it for?
Use it to calculate thermal expansion and temperature-dependent Gibbs energy from volume and vibration data.
Why use it?
It helps estimate thermal properties across temperatures when direct experiments or much more expensive calculations are not practical.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

Good fit Use it to calculate thermal expansion and temperature-dependent Gibbs energy from volume and vibration data.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/learningmatter-mit/atomisticskills/mat-qha-thermal-expansion
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add learningmatter-mit/AtomisticSkills --skill mat-qha-thermal-expansion
Clone the repo
git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for mat-qha-thermal-expansion

README.md
[![agentmods](https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-qha-thermal-expansion/github.svg)](https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-qha-thermal-expansion)
Your own site
<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-qha-thermal-expansion"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-qha-thermal-expansion/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for mat-qha-thermal-expansion

Your own site · 80×15
<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-qha-thermal-expansion"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-qha-thermal-expansion.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 992 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00026 $0.00992
Opus 5 $0.00013 $0.00496
Sonnet 5 $0.00005 $0.00198
Haiku 4.5 $0.00003 $0.00099

Measured 7d ago against content hash cbb368ee0e54, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

mat-qha-thermal-expansion scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/calculate_qha.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/mat-qha-thermal-expansion/SKILL.md · 88 lines

How it starts

The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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 must be installed in the relevant conda environment.

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:

source window volume width
matcalc QHACalc default scale_factors 0.95-1.05 linear 1.35x
atomate2 QhaMaker default linear_strain (-0.05, 0.05) 1.35x
phonopy Si-QHA example e-v.dat 140.03-189.07 A^3 1.35x

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.

Read the full file on GitHub · 88 lines

Files

What ships with it

5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago First seen · 88 lines · 26 tokens per session scan A cbb368ee0e54

Subscribe to this mod's changes

mat-qha-thermal-expansion is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (163 stars, last pushed 7d ago), licensed MIT. It adds 26 tokens to every session and 992 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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