Agent skill

Mat Dft Electron Phonon

by learningmatter-mit in learningmatter-mit/AtomisticSkills

Computes electron-phonon coupling to calculate temperature-dependent bandgap renormalization using atomate2.

MITAuto-check passed

Install Mat Dft Electron Phonon

skills CLI
$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-dft-electron-phonon -a claude-code

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

GitHub CLI
$ gh skill install learningmatter-mit/AtomisticSkills mat-dft-electron-phonon --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-dft-electron-phonon .claude/skills/mat-dft-electron-phonon && 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-dft-electron-phonon
GitHub stars
176
Token cost
~768 tokens
SKILL.md length
302 words
Files
4 (incl. scripts)
Skills in repo
129
Repo updated
First seen
Licence
MIT

At a glance

Computes electron-phonon coupling to calculate temperature-dependent bandgap renormalization using atomate2.

  • Works in 3 steps: Construct the Electron-Phonon Workflow → Job Execution → Parse Output
  • SKILL.md covers Goal, Background, Instructions and Examples, plus 2 more sections
  • Runs Python scripts from its folder

What it does

Mat Dft Electron Phonon is an agent skill from learningmatter-mit/AtomisticSkills. Computes electron-phonon coupling to calculate temperature-dependent bandgap renormalization using atomate2.

Its SKILL.md is about 770 tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts (for example `examples/silicon/README.md`, `examples/silicon/si_flow.json` and `scripts/generate_inputs.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

  • “Use the mat-dft-electron-phonon skill to compute electron-phonon coupling to calculate temperature-dependent bandgap renormalization using atomate2”
  • “/mat-dft-electron-phonon”

Requirements

  • Python 3

Workflow steps

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

  1. Construct the Electron-Phonon Workflow
  2. Job Execution
  3. Parse Output

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):

    • doi.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.

Context cost

Mat Dft Electron Phonon loads about 768 tokens when it runs. Until then it costs about 33 tokens; SKILL.md has 302 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~33
When it runs · the whole SKILL.md, loaded when a task matches
~768

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). 302 words, ~768 tokens.

Download SKILL.mdSave it as .claude/skills/mat-dft-electron-phonon/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
mat-dft-electron-phonon
description
Computes electron-phonon coupling to calculate temperature-dependent bandgap renormalization using atomate2.
metadata.category
materials
metadata.venv
cpu

mat-dft-electron-phonon

Goal

To determine the impact of electron-phonon coupling on the electronic eigenstates of a solid. At $T=0$ K, quantum fluctuations (zero-point motion) slightly perturb the geometric symmetry of a perfectly static lattice, causing a contraction known as zero-point renormalization (ZPR). As temperature increases, higher phonon modes dictate further eigenenergy shifts.

Background

Standard DFT predicts bandgaps under the Born-Oppenheimer limit (fixed infinite massive ions). Computing true temperature-dependent optical properties (photoluminescence shifting, exciton broadening) mandates adding back the phonon response. The ElectronPhononMaker calculates phonon modes first (via phonopy), generates properly thermalized stochastic structural snapshots respecting the true classical/quantum Bose-Einstein occupancies, and computes the static gap for each snapshot.

Instructions

1. Construct the Electron-Phonon Workflow

Generating the inputs uses the ElectronPhononMaker. You only need to provide the target primitive structure and the temperature list you want dynamically sampled.

bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/generate_inputs.py --output elph_flow.json
2. Job Execution

The default script serializes the Directed Acyclic Graph (DAG) logic. Because calculating robust phonon displacements involves constructing potentially hundreds of large supercell single-point DFT calculations, ensure you map this to an established HPC worker infrastructure (jobflow or Fireworks) rather than executing interactively locally.

3. Parse Output

The termination node evaluates the mean and variance of the bandgap/band edges from all stochastically distributed geometric snapshots at a given temperature, returning the renormalized gap.

Examples

Run the DAG generation for pristine primitive Silicon.

bash
cd ${CLAUDE_SKILL_DIR}/examples/silicon
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ../../scripts/generate_inputs.py --output si_flow.json

Constraints

  • Environments: Scripts require the cpu environment, which contains phonopy.
  • Phase Stability: The material must be strictly stable. If imaginary modes exist in the phonon branch, thermal displacement mapping will fail catastrophically since occupations of negative frequencies diverge.
  • Supercells: Accuracy is critically bound to taking a large enough supercell (supercell_matrix) to capture long-wavelength phonons correctly.

References

  • Zacharias, M., & Giustino, F. "One-shot calculation of temperature-dependent optical spectra and phonon-induced band-gap renormalization", Phys. Rev. B, 94, 075125 (2016). DOI

Author: Bowen Deng Contact: GitHub

© 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 3 other files (scripts) in skills/mat-dft-electron-phonon of learningmatter-mit/AtomisticSkills.

  • SKILL.md
  • examples/silicon/README.md
  • examples/silicon/si_flow.json
  • scripts/generate_inputs.py

Open the folder on GitHubat commit 6257444

Compare with similar skills

Mat Dft Electron Phonon 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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Dependency Updatecodewhale-hq/Codewhale41k—~142Automated safety check: PassMIT
Bump Sentry Dependencygetsentry/sentry46k—~815Automated safety check: PassCustom licence

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Questions about Mat Dft Electron Phonon

What does Mat Dft Electron Phonon do?

Computes electron-phonon coupling to calculate temperature-dependent bandgap renormalization using atomate2. Mat Dft Electron Phonon is an agent skill from learningmatter-mit/AtomisticSkills. Computes electron-phonon coupling to calculate temperature-dependent bandgap renormalization using atomate2.

How do I install Mat Dft Electron Phonon in Claude Code?

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

How do I install Mat Dft Electron Phonon in Codex?

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

Can I use Mat Dft Electron Phonon 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-dft-electron-phonon -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-dft-electron-phonon, .gemini/skills/mat-dft-electron-phonon, .github/skills/mat-dft-electron-phonon and .opencode/skills/mat-dft-electron-phonon in your project.

What does Mat Dft Electron Phonon need to run?

Going by SKILL.md and its folder, Mat Dft Electron Phonon needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Mat Dft Electron Phonon access the network?

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

Is Mat Dft Electron Phonon 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 Dft Electron Phonon use?

Mat Dft Electron Phonon 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 Dft Electron Phonon use?

About 768 tokens (SKILL.md is roughly 3.1k 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 Dft Electron Phonon?

Skills that share tags, products or a category with Mat Dft Electron Phonon: Dependency Scanning (sickn33/agentic-awesome-skills, 47k stars), Dependency Check (ruvnet/ruflo, 74k stars), Ito Compute (affaan-m/ECC, 275k stars) and Dependency Update (codewhale-hq/Codewhale, 41k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mat Dft Electron Phonon?

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.