mat-dft-vasp

mat-dft-vasp

Goal

To prepare VASP input files (INCAR, POTCAR, KPOINTS, POSCAR) locally for a structure or list of structures, and to parse the resulting VASP output files (vasprun.xml, OUTCAR) to extract the final energies, forces, stress, and geometries.

[!TIP] Atomate2 Recommendation: It is highly recommended to run VASP through the atomate2 MCP server/tools instead of manually using this skill. atomate2 natively handles automatic SLURM job submission, dynamic error handling and on-the-fly corrections, automated result parsing, and MongoDB cloud storage.

Instructions

Step 1. Prepare VASP Inputs

Use the prepare_vasp_inputs.py script to generate local input files from a structure (CIF, XYZ, POSCAR) or a directory of structures.

# Env: base-agent
python .agents/skills/mat-dft-vasp/scripts/prepare_vasp_inputs.py \
    <structure-path> \
    <output-dir> \
    --preset_type matpes-r2scan \
    --calculation_type relaxation

Parameters:

• structure_path: Path to a single structure or a directory of structures.
• output_dir: Location to write the inputs. If structure_path is a directory, subdirectories will be created.
• --preset_type: Standard VASP presets. Options include omat, mp, matpes-pbe, and matpes-r2scan.
• --calculation_type: Defaults to relaxation. Use static for SCF static single-point.

(Note: Once inputs are generated, you can submit the VASP jobs to an HPC or local cluster. If you instead want to run VASP jobs automatically through Jobflow on configured remote resources, consider using the mcp_atomate2_run_atomate2_vasp_calculation MCP tool).

Step 2. Parse VASP Results

After the VASP calculation has concluded, extract the output data (energy, forces, stress, structure) using parse_vasp_results.py. This handles both single directories (containing a vasprun.xml) and root directories with multiple subdirectories.

# Env: base-agent
python .agents/skills/mat-dft-vasp/scripts/parse_vasp_results.py \
    <vasp-output-dir> \
    --save_to_file parsed_results.json