autoWTE: Automatic heat-conductivity predictions from the Wigner Transport Equation

autoWTE employs foundation Machine Learning Interatomic Potentials and phono3py to determine the Wigner Thermal conductivity in crystals with arbitrary composition and structure.

Install

Clone repository:

git clone https://github.com/MPA2suite/autoWTE.git

Then install in editable mode:

pip install -e .

Pre-requisites (need to be installed seperately or added to PYTHONPATH)

• phono3py (see https://phonopy.github.io/phono3py/install.html for installation instructions)

Installed automatically during pip install:

• phonopy
• ase
• numpy
• matplotlib
• spglib
• tqdm
• h5py
• pandas

Usage

The example scripts showcase a sample workflow for testing a MACE potential and comparing the thermal conductivity with DFT calculations for a collection of different materials. The scripts may be modified easily to use any foundation Machine Learning Interatomic Potentials. See autoWTE/MLPS.py for calculator setup utilities.

1. Modify and execute 1_force_sets.py file in benchmark-scripts to obtain the force sets for second and third order force constants.
2. Modify and execute 2_thermal_conductivity.py file in benchmark-scripts to obtain the thermal conductivity results needed for the bencmark evaluation.
3. Modify and execute 3_evaluate.py file in benchmark-scripts to obtain the benchmark metrics (SRME) and results.

How to cite

@misc{póta2024thermalconductivitypredictionsfoundation,
      title={Thermal Conductivity Predictions with Foundation Atomistic Models}, 
      author={Balázs Póta and Paramvir Ahlawat and Gábor Csányi and Michele Simoncelli},
      year={2024},
      eprint={2408.00755},
      archivePrefix={arXiv},
      primaryClass={cond-mat.mtrl-sci},
      url={https://arxiv.org/abs/2408.00755}, 
}