VASP ASE Skill

This skill allows the agent to interface with VASP on high-performance computing (HPC) systems like the DGX A100. It focuses on using ASE as the wrapper for clean Pythonic control.

🛠 Prerequisites

• Environment Variables: VASP_PP_PATH must point to the directory containing potpaw_PBE, etc.
• Dependencies: ase and pymatgen (optional but recommended for analysis) must be installed in the (base) conda environment.
• VASP Executable: Access to vasp_std, vasp_gam, or vasp_ncl.

📜 Execution Instructions

1. Setup & Initialization

Always initialize the VASP calculator using the ase.calculators.vasp.Vasp class.

• Default Check: If the user doesn't specify, use xc='PBE', kpts=(4, 4, 4), and encut=520.
• Parallelization: On this DGX system, use mpirun or srun. Example command: command='mpirun -np 16 vasp_std > vasp.out'

2. Geometry Optimization Template

from ase.io import read
from ase.calculators.vasp import Vasp
from ase.optimize import BFGS

atoms = read('POSCAR')
calc = Vasp(directory='run_dir',
            command='mpirun -np 16 vasp_std',
            xc='PBE',
            encut=520,
            ismear=0,
            sigma=0.05,
            lreal='Auto',
            nsw=100,
            ibrion=2)
atoms.set_calculator(calc)
energy = atoms.get_potential_energy()

3. Error Handling & Troubleshooting

If a calculation fails, the agent should parse OUTCAR or stdout and apply these fixes:

🧪 Validation Step

Before running large productions, the agent should run a "Dry Run" by setting LSTOP = .TRUE. in the INCAR or running a single-point energy on a 2-atom cell to verify the environment pathing.

📂 Output Management

• Always capture energy_free, forces, and stress.
• Store results in a structured JSON or CSV for the user to download from the DGX server.