Screen and rank candidate polymers by predicted glass transition temperature (Tg) against a target value or window, orchestrating BigSMILES validation, polymer fingerprinting, and the Paramus BRAIN Tg model.When to use: a user provides repeat units (BigSMILES/SMILES) and asks which best hit a Tg target.Multi-tool WORKFLOW, not one call.
Paramus — Polymer Tg Screening
Overview
Validate structures, featurize them, predict Tg with the deterministic BRAIN model, and rank candidates by fit to a target. The value is the end-to-end screen with a materials-design decision rule — not any single prediction call.
Guidance vs execution: predictions come from the tested BRAIN Tg model with version + uncertainty attached. Do not fit or estimate Tg with hand-written code — route to the model.
When to Use
- User supplies polymers (BigSMILES/SMILES) and asks which match a target Tg
- Ranking monomer/copolymer options for a thermal target Do not use for: measured-data curve fitting, or properties other than Tg.
Workflow
Candidates + target Tg (or window)
↓ 1. Validate & normalize BigSMILES → Paramus polymer parsing tool
↓ 2. Polymer fingerprint / features → Paramus fingerprinting tool
↓ 3. Predict Tg (with uncertainty) → Paramus BRAIN Tg model
↓ 4. Score vs target → distance / inside-window test
↓ 5. Rank & report → ranked table + model version + uncertainty
Procedure
- Discover tools via
search("BigSMILES validation", "polymer fingerprint", "Tg prediction"); confirm withget_schema. - Validate every BigSMILES; report malformed inputs explicitly — never silently skip.
- Featurize the validated set (batched if supported) so all share one feature convention.
- Predict Tg; capture predicted Tg + uncertainty + units + model version.
- Score against target (window → inside/outside; point → |Tg_pred − Tg_target|). State the rule.
- Report: polymer, Tg ± uncertainty, fit-to-target, rank, model version.
Domain Judgment
- Uncertainty gates ranking: an error bar straddling the window boundary is "uncertain-fit", not #1.
- Extrapolation warning: structures far from the model's applicability domain → demote confidence.
- Never mix predictions across model versions in one ranking.
Fallbacks
- BRAIN Tg model unavailable → report; offer nearest thermal-property tool. No local group-contribution hack.
- Endpoint unreachable → stop and report; do not fall back to local prediction code.
Tools this skill may use
Candidate deterministic tools an agent is likely to route to when running this skill. The skill decides which to call at runtime.
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Brain AI Polync Predict Tg
Polymer SciencePredict glass transition temperature (Tg) for polymers using ML -
Get Polymer Fingerprint
Polymer ScienceGenerates a molecular fingerprint for a polymer structure using the polymerfingerprint library. Fingerprints are numerical representations that encode structural features, enabling computational analysis, similarity searches, and machine learning applications. Supports BigSMILES notation for representing polymer structures. -
Validate Bigsmiles
Polymer ScienceValidate BigSMILES polymer notation strings. Checks if a string is valid BigSMILES notation and whether it contains stochastic polymer objects (using {} blocks with bonding descriptors). Returns detailed validation information including is_valid, is_bigsmiles (has stochastic objects), and any parsing errors. -
Brain AI Transpolymer Embed
Polymer ScienceGenerate a 768-dimensional polymer embedding vector from a SMILES or BigSMILES string using the TransPolymer model.
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Used in these use cases
Customer scenarios that orchestrate this skill end-to-end.