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VASP / ISAAC

Domain: Materials science — DFT input/output handling with VASP

Source: use_cases/isaac_vasp/

Demo bundle: Download .tar.gz

Convert VASP DFT input/output into an AI-ready ISAAC record — register the conversion tooling and build the pipeline against bundled NEB fixture data (no DFT run, no HPC).

Estimated time: ~15 minutes — the NEB fixture data is bundled in this folder, so the only real cost is a one-time pip install pymatgen. No DFT run, no HPC.

Goal: register a converter as a DSAgt code, then have the agent run it through dsagt-run to turn a VASP nudged-elastic-band (NEB) calculation into an ISAAC AI-Ready Record — with the execution captured in trace_archive/.

  • Converter: vasp_neb_to_isaac.py parses the NEB OUTCARs with pymatgen.io.vasp and emits a v1.05 record.
  • Data: the neb/00..04/ subdirs are a small NEB fixture (5 images) copied from the pymatgen test suite — bundled here so the demo runs without a DFT code.
  • Schema: isaac_record_v1.json.

For the skill-management counterpart of this workflow (the agent discovers, installs, and authors the converter as a skill on tiny mock data), see the sister demo isaac_skills_demo.

Prerequisites

  • DSAgt installed (uv sync --all-groups) and an agent platform installed and already authenticated (BYOA — dsagt writes no credentials; the default local embedder needs no API key).
  • pymatgen importable in the environment dsagt runs in (uv pip install pymatgen) — the converter uses pymatgen.io.vasp.

Setup

uv pip install pymatgen                 # the converter's one real dependency
dsagt init isaac-vasp --agent claude
PROJ=~/dsagt-projects/isaac-vasp
mkdir -p "$PROJ/codes/scripts"
cp use_cases/isaac_vasp/vasp_neb_to_isaac.py "$PROJ/codes/scripts/"
cp -r use_cases/isaac_vasp/neb "$PROJ/data_neb"
dsagt start isaac-vasp

Execution

Paste each prompt into the agent, one at a time.

1. Register the converter as a code

Register a code named vasp-neb-to-isaac. Its executable is
`python codes/scripts/vasp_neb_to_isaac.py`, which takes a positional NEB
directory argument (containing 00/, 01/, ... image subdirs) and an optional
`--output` path. Run it with `--help` first to confirm the interface, then save
the code spec with the positional `neb_dir` and the `--output` option.

Verify: Search the registry for the vasp-neb-to-isaac code. → $PROJ/codes/vasp-neb-to-isaac/SKILL.md should exist.

2. Run the conversion through dsagt-run

Using the registered vasp-neb-to-isaac code, convert the NEB calculation in
data_neb/ and write the ISAAC record to data_neb/isaac_neb_record.json. Use the
exact dsagt-run command from the spec so the execution is recorded. Then tell me
the record's reaction-energy / barrier fields and how many images it summarized.

Expect: the agent runs dsagt-run --code vasp-neb-to-isaac -- python codes/scripts/vasp_neb_to_isaac.py data_neb/ --output data_neb/isaac_neb_record.json, pymatgen parses the five OUTCARs, and a v1.05 ISAAC record lands with the computation / measurement blocks populated (5 NEB images). Compare against the reference isaac_neb_record.json shipped here.

3. Reconstruct the pipeline

Reconstruct the pipeline from the execution records as a script.

Post-Conditions

  1. Code registry contains the vasp-neb-to-isaac spec (codes/vasp-neb-to-isaac/SKILL.md).
  2. trace_archive/ holds the conversion's provenance record.
  3. data_neb/isaac_neb_record.json is a valid ISAAC v1.05 record matching the shape of the bundled isaac_neb_record.json.
  4. MLflow traces (in the serverless mlflow.db store) capture the run — mlflow ui --backend-store-uri sqlite:///$PROJ/mlflow.db.

Cleanup

dsagt rm isaac-vasp -y

Notes

  • The neb/ OUTCARs are public pymatgen test fixtures vendored here for a self-contained demo. They are large (~32 MB total); a future revision may fetch them on demand instead of shipping them in-repo.
  • The bundled skills/vasp-to-isaac/ skill is a broader slab/bulk converter (a different pymatgen workflow that needs vasprun.xml-bearing slab/bulk data, not the NEB fixture here). It's shipped as a reference skill; the NEB converter above is what this folder's data actually exercises.