mat-dft-electron-phonon
SkillDev toolsComputes electron-phonon coupling to calculate temperature-dependent bandgap renormalization using atomate2.
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What this skill tells your AI
The instructions your AI receives, as published by learningmatter-mit/atomisticskills in .agents/skills/mat-dft-electron-phonon/SKILL.md and read by ahel’s review.
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.
# Env: atomate2-agent
python .agents/skills/mat-dft-electron-phonon/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.
# Env: atomate2-agent
cd .agents/skills/mat-dft-electron-phonon/examples/silicon
python ../../scripts/generate_inputs.py --output si_flow.json
Constraints
- Environments: Scripts require the
atomate2-agentenvironment containingphonopy. - 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
Signals
- GitHub stars
- 164
- Forks
- 24
- Last commit
- Sep 2026
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mat-dft-electron-phonon- Source
- github.com/learningmatter-mit/atomisticskills