skills/ontology/ontology-mapper/SKILL.md
Map materials science terms, crystal structures, and sample descriptions to standardized ontology classes and properties — resolve natural-language concepts to ontology entries with confidence scores, translate Bravais lattice types, space groups, and lattice constants into ontology-compliant annotations, and produce full sample metadata from structured descriptions. Supports any ontology in ontology_registry.json (CMSO, ASMO, etc.). Use when annotating simulation inputs with FAIR metadata, translating "BCC iron" or "FCC copper" into formal ontology terms, preparing machine- readable sample descriptions, or bridging between lab vocabulary and ontology vocabulary, even if the user only says "what CMSO terms describe my material" or "annotate this sample for me."
npx skillsauth add HeshamFS/materials-simulation-skills ontology-mapperInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Translate real-world materials science descriptions into standardized ontology annotations. Given terms like "FCC copper" or structured data like {"material": "iron", "structure": "BCC", "lattice_a": 2.87}, produce the corresponding ontology classes and properties for any registered ontology.
ontology_registry.json<name>_mappings.json) for ontology-specific synonyms and labels| Input | Description | Example |
|-------|-------------|---------|
| Ontology | Ontology name from registry | cmso, asmo |
| Term(s) | Natural-language materials concept(s) | "unit cell", "FCC,copper,lattice" |
| Crystal system | One of the 7 crystal systems | cubic, hexagonal |
| Bravais lattice | Lattice type (symbol or common name) | FCC, cF, BCC |
| Space group | Space group number (1-230) | 225 |
| Lattice parameters | a, b, c in angstroms; alpha, beta, gamma in degrees | a=3.615 |
| Sample description | JSON dict with material properties | {"material":"copper","structure":"FCC"} |
What do you need to map?
├── A concept or term to find its ontology class
│ └── concept_mapper.py --ontology <name> --term "<term>"
├── Crystal structure parameters to ontology terms
│ └── crystal_mapper.py --ontology <name> --bravais <type> --space-group <N> --a <val>
├── A full sample description to ontology annotations
│ └── sample_annotator.py --ontology <name> --sample '<json>'
└── Multiple terms at once
└── concept_mapper.py --ontology <name> --terms "term1,term2,term3"
| Script | Key Outputs |
|--------|-------------|
| scripts/concept_mapper.py | results.matches, results.unmatched, results.suggestions |
| scripts/crystal_mapper.py | results.ontology_classes, results.ontology_properties, results.effective_system, results.bravais_lattice, results.validation_warnings |
| scripts/sample_annotator.py | results.annotations, results.sample_type, results.material_type, results.unmapped_fields, results.suggested_properties |
concept_mapper.py to find matching ontology classes.crystal_mapper.py to map them and validate constraints.sample_annotator.py to produce full ontology annotations.unmapped_fields and suggested_properties for completeness.User: I'm setting up an MD simulation of BCC iron with lattice parameter 2.87 angstroms.
What CMSO terms should I use?
Agent: Let me map your iron sample to CMSO ontology terms.
[Runs: sample_annotator.py --ontology cmso --sample '{"material":"iron","structure":"BCC","lattice_a":2.87,"space_group":229}' --json]
Your BCC iron simulation maps to these CMSO annotations:
- **Sample**: Atomic Scale Sample (subclass of Computational Sample)
- **Material**: Crystalline Material
- **Unit Cell**: Bravais lattice = "cI" (body-centered cubic)
- **Space Group**: number = 229 (Im-3m)
- **Lattice**: a = 2.87 Å
- **Element**: Fe
Suggested additions:
- Number of atoms in the simulation cell
- Simulation cell vectors and angles
# Map a single concept
python3 skills/ontology/ontology-mapper/scripts/concept_mapper.py \
--ontology cmso --term "space group" --json
# Map multiple terms
python3 skills/ontology/ontology-mapper/scripts/concept_mapper.py \
--ontology cmso --terms "FCC,copper,lattice constant" --json
# Map crystal parameters (with ontology-specific labels)
python3 skills/ontology/ontology-mapper/scripts/crystal_mapper.py \
--ontology cmso --bravais FCC --space-group 225 --a 3.615 --json
# Map crystal parameters (generic labels, no ontology specified)
python3 skills/ontology/ontology-mapper/scripts/crystal_mapper.py \
--bravais FCC --space-group 225 --a 3.615 --json
# Annotate a full sample
python3 skills/ontology/ontology-mapper/scripts/sample_annotator.py \
--ontology cmso \
--sample '{"material":"copper","structure":"FCC","space_group":225,"lattice_a":3.615}' \
--json
To support a new ontology (e.g., ASMO), create a <name>_mappings.json in references/:
{
"ontology": "asmo",
"synonyms": { "simulation method": "Simulation Method", ... },
"property_synonyms": { "timestep": "has timestep", ... },
"material_type_rules": { "keyword_rules": [...], "default": "Material" },
"sample_schema": { "sample_class": "Simulation", ... },
"crystal_output": { "base_classes": [...], "property_map": {...} },
"annotation_routing": { "unit_cell_indicators": [...], ... }
}
Then add "mappings_file": "asmo_mappings.json" to the ontology's entry in ontology_registry.json. No code changes needed.
| Error | Cause | Resolution |
|-------|-------|------------|
| space_group must be between 1 and 230 | Invalid space group number | Use a valid space group number |
| a must be positive | Non-positive lattice parameter | Provide positive values in angstroms |
| Sample must be a non-empty dict | Empty or missing sample data | Provide a valid JSON sample dict |
| Validation warnings | Lattice parameters inconsistent with crystal system | Check that a=b=c for cubic, etc. |
--ontology is validated against registered ontology names in ontology_registry.json (fixed allowlist)--term and --terms are length-limited and used only for substring matching against pre-processed synonym tables (never interpolated into code)--bravais is validated against a fixed set of recognized lattice type symbols--space-group is validated as an integer between 1 and 230--a, --b, --c, --alpha, --beta, --gamma) are validated as finite positive numbers--sample JSON is parsed with json.loads() and validated as a non-empty dict; keys and values are type-checkedreferences/ directory: ontology_registry.json, *_mappings.json, *_summary.json, crystal_systems.json, element_data.json (all read-only)eval(), exec(), or dynamic code generation| Date | Version | Changes | |------|---------|---------| | 2026-02-25 | 1.1 | Refactored for multi-ontology support: externalized CMSO-specific knowledge to config | | 2026-02-25 | 1.0 | Initial release with CMSO mapping support |
development
Plan verification and validation campaigns for simulation codes using manufactured solutions, canonical benchmark problems, grid/time refinement, uncertainty propagation, and pass/fail acceptance criteria. Use when an agent needs to prove a solver, model, or result is trustworthy rather than only plausible.
testing
Map computational materials tasks onto workflow engines such as atomate2, jobflow, AiiDA, pyiron, or a simple one-off script. Use when deciding how to structure a reproducible campaign, DAG, restart strategy, provenance record, storage layout, or migration path from ad hoc scripts to managed workflows.
development
Plan molecular dynamics post-processing for materials simulations, including RDF, MSD and diffusion, VACF/VDOS, coordination numbers, bond-angle distributions, stress-strain curves, equilibration detection, PBC unwrapping, and trajectory format choices. Use before writing MD analysis scripts or trusting trajectory-derived results.
development
Triage cross-code simulation failures and propose safe retry ladders for nonconvergence, NaN/Inf, exploding energies, unstable timesteps, pressure blow-up, missing potentials, bad pseudopotentials, corrupted output, and incomplete runs. Use when an agent sees a failed or suspicious materials simulation and needs a defensible first response.