This repo contains a library that aims at automatizing some Density Functional Theory (DFT) workflows in VASP by using the ASE toolkit. The workflows are losely based on the wonderful ressources provided by Prof. John Kitchin. I only implement what I need for my own research and I share it here in the hope that it might benefit others.
This repo has been entirely revamped on 11/05/2023. You can find the legacy version of ASE_VASP_automation here.
- To install the ASE_VASP_automation software, paste this folder wherever it is convenient for you on your computer and add export PYTHONPATH=<path-to-ase_vasp_automation-package>:$PYTHONPATH to your ~/.bashrc file.
- You might want to use the spec file to install all required python packages into a virtual conda environment by running conda create --name env_vasp_ase_automation --file spec-file.txt.
- You need to have ASE properly installed and set up to communicate with VASP.
- You need to have a properly working and licensed VASP installation on your local computer or a high performance cluster.
- The scripts can be run from anywhere, meaning that you could cut and paste the example folder to wherever you store your code.
The program is build to run simple tasks that are defined within .json files. Within the example folder, few scripts are provided in which workflows are defined. Workflows comprise a certain order of tasks that are conducted on defined atomic structures given a set of parameters. The folder settings contains settings files that can be used in conjunction with different workflows.
The currently only provided example is on stacking fault energy determination using the axial interaction model (see our recent paper on its application to Fe-C, Fe-N and austenitic stainless steel). Here 4 different relaxation modes are offered: volume relaxation, relaxation of the c/a ratio, volume relaxation and relaxation of the c/a ratio, and no relaxation at all. Finally, the SFE value is determined using the axial interaction model.
The scripts can be run as-is on a personal computer, by default it will do so with kpar = 1 and ncore = 1. If you have VASP and ASE installed on a High Performance Cluster (HPC), or if you have a parallelized version of VASP compiled on your personal computer, you can supply the arguments kpar and ncore on the command line. As an example, you can run the calculations with kpar = 2 and ncore = 16 by executing: job.py 2 16
If you want to run VASP in a parallel environment, you will have to set the ASE environment variable ASE_VASP_COMMAND to "mpirun vasp_std".
If you find any errors, feel free to submit a pull request or to adress them in the "Issues" tab. If you want to contribute to this code, please get into contact with me. Also feel tree to fork the code and develop your own alterations.