This workflow is a part of AI Square. We want to refactor the autotest code based on dflow. This is "general property test" (elastic parameters, EOS, surface energy, interstitial energy, vacancy energy and stacking fault energy supported so far) using VASP, LAMMPS, or ABACUS.

pip install "git+https://github.com/deepmodeling/APEX.git"

You can go to the example folder and there are some examples for reference. You can go to one of them and fill in the global.json file. Then you can submit the workflow.

If you want to use VASP code to do the DFT autotest, like the folder vasp_demo. You need to prepare INCAR, POTCAR, POSCAR, global.json(notice that json files for relaxation and properties task are needed as input arguments), then ：

apex param_relax.json para_props.json

If you want to run only relaxation or only property tests (notice that property tests require relaxation results under corresponding path in ./confs), for example for relaxation, just give one argument like:

apex param_relax.json

For property tests,

apex param_props.json

If you want to use ABACUS code, like the folder abacus_demo. You need to prepare INPUT, STRU, *.UPF, global.json, param_relax.json, param_props.json (notice that *.orb and KPT are optional ), then：

apex param_relax.json param_props.json

If you want to use LAMMPS to do MD calculation, like the folder lammps_demo. You need to prepare POSCAR, frozen_model.pb, global.json, param_relax.json, param_props.json, then:

apex param_relax.json param_props.json

You can monitor the workflow process on the website.