Density Functional Theory

These examples demonstrate kALDo workflows using density functional theory (DFT) calculations. DFT provides highly accurate interatomic force constants from first principles, making it the gold standard for predicting thermal conductivity in crystalline materials. The workflows shown here use Quantum ESPRESSO for DFT calculations and D3Q or thirdorder.py for computing anharmonic (3rd order) force constants.

Setup Instructions

Requirements

Before running these examples, install the following packages:

GPU/CPU Configuration

For TensorFlow-based calculations, you can specify GPU or CPU usage following these instructions.

List and content of examples folder

For each example, more detailed information is provided by the README.md file contained in the corresponding directory.

  • germanium_dft_d3q: This example illustrates how to perform thermal transport simulation for a germanium diamond (2 atoms per cell) with D3Q package as force calculator.

  • magnesium_oxide_dft_d3q: This example illustrates how to perform thermal transport simulation for a rock-salt MgO (2 atoms per cell) system using D3Q package as force calculator.

  • silicon_dft_qe: This example illustrates how to perform thermal transport simulation for a silicon diamond (2 atoms per cell) system using QE package as force calculator.

Git Large File Storage (LFS)

This repository uses Git LFS to handle large files. Ensure Git LFS is installed on your system by following the instructions on the Git LFS website.

Once installed, clone the repository as usual with git clone — large files will be downloaded automatically. If you’ve already cloned without Git LFS, retrieve the large files with:

git lfs pull