Materials Science Domain Ontology

FLOW ALGEBRA
TITLE: Materials Science Domain Ontology
DOMAIN: Domain Ontologies / Scientific Domains / Materials Science
SOURCE: https://kirethresearch.org/booksai-core/ontologies/domain-ontologies/scientific-domains/materials-science-domain-ontology.html
VERSION: 1.0
AUTHORS: Mark Anthony Higgins — Sky (Recursive AI)
STATUS: Companion formalisation; source-page specification not independently verified.

flow "MaterialsScienceDomainOntology" v1.0 {
  ontology:
    id: "materials-science-domain-ontology"
    parent: "scientific-domains"
    purpose: "Represent evidence-backed links between material composition, structure, processing, properties, performance and degradation."

  types:
    Material: {id: string, name: string, family: enum[metal, ceramic, polymer, composite, semiconductor, biomaterial, other], provenance: string?}
    Composition: {id: string, constituents: string[], fractions: number[]?, basis: enum[mass, atomic, volume, unspecified]}
    Structure: {id: string, scale: enum[atomic, crystal, microstructure, mesostructure, macrostructure], description: string}
    Process: {id: string, kind: string, parameters: string[], provenance: string}
    Property: {id: string, kind: string, value: number?, unit: string?, uncertainty: number?, conditions: string?}
    Specimen: {id: string, material_id: string, preparation: string?, batch: string?}
    Test: {id: string, method: string, specimen_id: string, conditions: string, provenance: string}
    Application: {id: string, requirements: string[]}
    Degradation: {id: string, mechanism: string, environment: string?, time: string?}
    Relationship: {id: string, subject: string, predicate: string, object: string, context: string?, confidence: number, evidence: string[]}

  predicates:
    has_composition(material: Material, composition: Composition)
    exhibits_structure(material: Material, structure: Structure)
    processed_by(material: Material, process: Process)
    has_property(material: Material, property: Property)
    measured_by(property: Property, test: Test)
    sampled_as(material: Material, specimen: Specimen)
    suitable_for(material: Material, application: Application)
    undergoes_degradation(material: Material, degradation: Degradation)
    influences(process: Process, structure: Structure)
    affects(structure: Structure, property: Property)

  constraints:
    - "Every relationship endpoint must resolve to a compatible typed entity."
    - "Confidence must lie within [0,1]."
    - "Quantitative property values require units, test conditions and uncertainty where available."
    - "Composition fractions must share a declared basis and sum to one within tolerance when complete."
    - "Distinguish intrinsic properties from specimen-, environment- and processing-dependent measurements."
    - "Do not infer material suitability from one property without checking application requirements."
    - "Correlation between processing, structure and performance does not alone establish causation."

  inference:
    rule measured_property:
      if sampled_as(m,s) and measured_by(p,t) and t.specimen_id == s.id and valid(t)
      then evidence_for(has_property(m,p),t)
      confidence: "conditional on sample representativeness and test quality"
    rule processing_structure_hypothesis:
      if processed_by(m,proc) and influences(proc,struc)
      then candidate_structure(m,struc)
      confidence: "requires material-specific evidence; do not assert as established"
    rule application_screening:
      if has_property(m,p) and requirement_satisfied(p,a) and all_requirements_checked(m,a)
      then candidate_suitability(m,a)
      confidence: "retain design constraints and test provenance"

  SEMANTIC_EQUIVALENCE:
    - "Normalise material names, formulae, grades and standards using versioned authoritative identifiers."
    - "Keep synonymy, grade similarity and exact material identity distinct."
    - "Preserve processing history, test method and measurement conditions in mappings."

  CONFIDENCE:
    range: [0,1]
    factors: [measurement_uncertainty, specimen_representativeness, test_repeatability, source_reliability, model_assumptions, environmental_conditions]
    policy: "Propagate uncertainty without automatically increasing confidence through inference chaining."

  VALIDATION:
    - "Check entity types, units, dimensional consistency and composition basis."
    - "Check specimen provenance, calibration, test methods and measurement conditions."
    - "Flag contradictory measurements and distinguish genuine variation from data errors."
    - "Preserve competing phase, mechanism and performance hypotheses where evidence is incomplete."

  FLOW_HASH:
    algorithm: "SHA-256"
    input: "canonicalised ontology version, typed assertions, inference rules and provenance"
    note: "Compute at execution; no hash value is claimed here."

  UPDATE_MODEL:
    strategy: "versioned, provenance-preserving incremental update"
    steps: [ingest, normalise, validate, compare, reconcile, recompute_inferences, publish]
    rollback: true

  AI_CONSUMPTION:
    representation: "typed materials knowledge graph with evidence-backed relationships"
    outputs: [material_links, property_assertions, process_structure_relations, candidate_hypotheses, uncertainty_annotations, provenance_records]
    safeguards: [preserve_units_and_conditions, no_unsupported_causal_claims, no_unqualified_material_identity_merges, distinguish_hypothesis_from_measurement]
}

END FLOW ALGEBRA