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mat-qha-thermal-expansion

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

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How to use this skill

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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. Calculation Workflow

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

conda activate matgl-agent
python .agents/skills/qha/scripts/calculate_qha.py \
    --structure path/to/relaxed_structure.cif \
    --model_type matgl \
    --eos vinet \
    --output_dir research/my_folder/qha

4. Output Files

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

5. Examples

See examples/ for detailed usage scenarios.

Author: Bowen Deng Contact: GitHub @learningmatter-mit