Paramus — Polymer Mechanical Property Screen

Overview

Predict mechanical properties for a candidate set and rank against a performance spec. The value is the multi-property spec-matching, not a single modulus prediction.

Guidance vs execution: mechanical predictions come from tested BRAIN models with provenance and uncertainty. Do not estimate moduli with hand-written correlations.

When to Use

• "Which of these polymers meets a stiffness/strength/toughness spec?"
• Ranking candidates for a mechanical performance target Do not use for: FEA / part-level simulation (hand off to the engineering engine).

Workflow

Candidates + mechanical spec (targets/limits) + property weights
  ↓ 1. Validate & featurize            → Paramus polymer parsing + fingerprinting
  ↓ 2. Predict mechanical properties   → Paramus BRAIN models (modulus, tensile, elongation…)
  ↓ 3. Check vs spec                    → pass/fail per property + margin
  ↓ 4. Score & rank                     → weighted or Pareto across properties
  ↓ 5. Report                           → ranked table + margins + uncertainty + model versions

Procedure

1. Discover tools with search/get_schema ("mechanical property prediction", "polymer fingerprint").
2. Validate and featurize the candidate set with one convention.
3. Predict each mechanical property; capture value + uncertainty + units + model version.
4. Test each candidate against the spec; compute the margin per property.
5. Rank by weighted objective (if weights given) or Pareto front; state the rule.
6. Report ranked candidates, per-property margins, and flag any pass that is within uncertainty of failing.

Domain Judgment

• A "pass" within the prediction error bar is a "marginal pass" — flag it, don't call it safe.
• Toughness is often a proxy; state which proxy the model uses.
• Note candidates outside the models' applicability domain.

Fallbacks

• A mechanical model missing → rank on available properties; mark the missing dimension.
• Endpoint unreachable → stop and report; no local correlations.