User Guide¶
In this page you may found different tutorials and exercises that will help you in the usage of BigDFT. The BigDFT code is a standard executable that can be run from the command line by passing an input file. However, the preferred way to drive BigDFT calculations is from its high level PyBigDFT interface. PyBigDFT will allow you to construct the system you wish to study, perform calculations with the BigDFT code, and extract scientific data from the results.
Jupyter notebooks are interactive lab notebooks that can run python code.
Each of the tutorials presented here is built as a jupyter notebook. We
encourage you to test out these notebooks on your own computer and to
build on top of them by copying one from the bigdft-doc folder of the code.
Environment Variables¶
Before running the code, you will want to configure your system path to include the bigdft executable and libraries. This can be accomplished with:
source install/bin/bigdftvars.sh
Before running the code, you might want to manually set a few more variables
manually. The variable BIGDFT_MPIRUN is the mpirun command you wish to
use (i.e. mpirun -np 2). OMP_NUM_THREADS should also be set if you
wish to enable multithreading.
Tutorials¶
Building Systems Programmatically¶
Running The Code¶
- Basics of BigDFT: first runs and managing different calculations, N2 molecule as example
- The overall structure of files in the disk
- Solution of N2 exercise: Handling the log files
- Usage of SystemCalculator Instance
- Usage of Dataset class
- Running a wavelet computation on a methane molecule
- The wavelet basis set, a convergence study
- Construction of the input dataset dictionaries
- Running a wavelet computation on a methane molecule, with AiiDa
- Construction of the input dataset dictionaries