dftlibs-qmckl-demo

This repository contains Jupyter notebooks (in Markdown .md format), which demonstrate the use of the open-source libraries like qmckl, numgrid, trexio and xcfun via their Python APIs. It takes advantage of the high-performance optimization of the underlying libraries (C, C++, Rust) and their native compatibility with Python.

Contents

• qmckl_numgrid.md
  1. Load some quantum chemistry data including molecular geometry and basis set information stored in the trexio file format via the qmckl context
  2. Set up a grid for molecular integrals via the numgrid library using the pre-processed data from the qmckl context
  3. Compute atomic orbital (AO) values on a grid via the high-performance qmckl routines
  4. Calculate the AO overlap matrix (via the qmckl_dgemm or NumPy matrix multiplication routines)
  5. Compute density matrix using the molecular orbital (MO) coefficients from the context
  6. Calculate numerically the number of electrons and compare it with the exact value from the context
  7. Compute density values on a grid (required for the DFT)
  8. Set up the exchange-correlation functional via the xcfun library
  9. Compute energy and XC potential values on a grid (via the xcfun)
  10. Calculate numerically the XC energy contribution

Requirements

• python3 (>= 3.6)

• jupyter

• jupytext (to convert notebooks from the Markdown .md format into.ipynb notebook)

• numpy (>= 1.19.3)

• trexio (Python API installation: pip install trexio)

• numgrid (Python API installation: pip install numgrid)

• qmckl (Python API installation instructions)

• xcfun (Python API installation instructions)

Note: we recommend to use virtual environments to avoid compatibility issues and to improve reproducibility. For more details, see the corresponding part of the Python documentation.

Running the notebook

To obtain a local copy of the .ipynb files:

• Clone the repository
• jupytext --to notebook qmckl_numgrid.md
• jupyter notebook qmckl_numgrid.ipynb

Additional steps needed to run a custom virtual environment in Jupyter notebooks

In some cases, it may happen that the Jupyter kernels in the activated virtual environment (e.g. myvenv) still point to the system-wide python binaries and not to the environment ones. This will result in ImportError when importing custom packages in the notebook cell. In order to avoid this, the myvenv has to be installed as an additional kernel.

This requires ipykernel python package, which usually comes together with the Jupyter installation. If this is not the case, run pip install ipykernel. You can install myvenv as a kernel by executing the following command:

python3 -m ipykernel install --user --name=myvenv

Now you can launch a Jupyter notebook. Once it is open, make sure that your virtual environment is selected as the current kernel. If this is not the case, try this:

1. Press the Kernel button in the navigation panel
2. In the output list of options select Change kernel
3. Find the name of your virtual environment (e.g. myvenv) in the list and select it

To uninstall the kernel named myvenv, execute the following command:

jupyter kernelspec uninstall myvenv