Mace Screening And Relaxation

2. Keep input and output trees separate

• Both tools reject output_root inside input_dir.
• The runtime stages a temporary batch tree under output_root, dispatches remotely, collects outputs, then removes the staging tree.

3. Use collected evidence, not launch success alone

• Returned metadata includes batch_state_rel, collected stdout/stderr/status files, and any batch_summary_rel.
• On dispatch failure, the tool still tries to collect partial outputs; inspect those before deciding to rerun.

4. Use this skill while the workflow artifact is still materials-side

• Use this skill for structure batches, candidate ranking, geometry cleanup, and materials-side post-analysis before expensive reference calculations.
• If the screening run produces a shortlist that should become a training dataset or an active-learning update, hand off that artifact to the ML skills as the next step.

Method-critical defaults

• For adsorption-energy screening on slabs, do not silently inherit the tool default for dispersion; choose it explicitly.
• Keep the dispersion setting consistent across clean slab, gas-phase reference, and adsorbed structures when the comparison depends on relative adsorption energies.
• Unless the user explicitly asks for a no-dispersion baseline, prefer enabling dispersion when surface-adsorbate interactions or ranking sensitivity may depend on it.
• Always report whether dispersion was enabled.
• If a screening stage is intended only as a cheap geometry triage rather than an energy-ranking stage, say so explicitly.
• Treat default_dtype=float64 as the conservative default for geometry relaxation. If you deliberately downgrade to float32 for speed, say so explicitly in the run summary.

Output Contract

Return:

• chosen MACE stage (relax or sp)
• output_root_rel
• batch_state_rel
• shortlist or keep/drop rule for downstream VASP handoff

References

• Use vasp-input-preparation only after a MACE shortlist exists; do not send the whole raw candidate pool forward by default.
• When the loop is ready for dataset building or retraining, hand off to mace-dataset-curation and active-learning-relabel-loop.