AlloyGraph : A Knowledge‐Graph‐Grounded Multiagent System for Nickel‐Based Superalloy Design Support

ABSTRACT Evaluations of AI systems for alloy design are built on commercial data sources in which metallurgically identical grades circulate under different vendor trade names, a leakage that name‐based deduplication cannot detect. We document this hazard and propose compositional‐distance stratification as the corrective, demonstrated on AlloyGraph, an open‐source nickel‐based superalloy design‐support system coupling an RDF knowledge graph, whose OWL 2 DL ontology serves as schema and ingestion‐time data validation, with physics‐informed machine learning and a multiagent LLM layer, with processing route and wrought processability constraints enforced deterministically inside the design pipeline, before any design is evaluated or reported. A compositional audit found 13 of 88 nominally unseen evaluation alloys to be near‐duplicates of training alloys under vendor branding, so all results are stratified by compositional novelty. The stratification attributes accuracy to distinct mechanisms: knowledge‐graph anchoring acts as exact retrieval, reducing yield strength error by 30% on near‐duplicate alloys through a few sparse anchoring events on five alloys, and essentially nothing beyond them; the agent layer, arbitrating between machine‐learning and physics estimates, improves yield strength by 18% on compositionally new alloys, stable across five seeded campaigns, a gain concentrated in solid‐solution‐strengthened alloys and absent for ‐strengthened alloys beyond the near‐duplicates; and deterministic physics corrections carry elastic modulus. Negative results are reported with equal prominence: the agents degrade elastic modulus, and in a surrogate‐scored in silico evaluation the LLM Designer's contribution to inverse design is not demonstrated against an informed sampling baseline. The deployed platform uses open‐access data and open‐weights models throughout, with per‐prediction conformal uncertainty intervals and automated provenance verification; commercial models appear only as external baselines and as an evaluation judge.