# Mat Dft Vasp

> Prepare VASP input files, run DFT calculations (locally or remotely via atomate2), and parse VASP output results.

- Skill: `learningmatter-mit/mat-dft-vasp` (Agent Skill, multi-file: 7 files)
- Install (CLI): `npx skillmds@latest add learningmatter-mit/mat-dft-vasp`
- Raw SKILL.md: https://api.skillmd.com/api/skills/learningmatter-mit/mat-dft-vasp/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: learningmatter-mit (https://skillmd.com/u/learningmatter-mit)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/learningmatter-mit/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.

```bash
# 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.

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


## Constraints
- **Environments**: The scripts require the `base-agent` Conda environment.
- **Parsing Robustness**: The parser requires at a minimum `vasprun.xml` to succeed. `OUTCAR` is read supplementary.
- **POTCARs**: Note that `prepare_vasp_inputs.py` relies on `pymatgen` to write POTCAR files, which requires your `PMG_DEFAULT_FUNCTIONAL` or `.pmgrc.yaml` to point to a valid POTCAR directory.

## References
- Kresse, G. & Furthmüller, J., "Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set". *Physical Review B*, 54, 11169. [DOI](https://doi.org/10.1103/PhysRevB.54.11169)

---

**Author:** Bowen Deng
**Contact:** [GitHub @learningmatter-mit](https://github.com/learningmatter-mit)