MATAI: A Unified Interactive Platform for AI-Driven Alloy Discovery

AI-driven materials discovery is increasingly bottlenecked not by individual models but by the fragmentation of the surrounding tooling: composition–property databases, property predictors, and constrained optimisers typically live in disjoint projects, forcing researchers to hand-stitch scripts, file formats, and provenance. We present MATAI, a unified, browser-based platform that integrates visual data analytics, composition-aware property prediction, and simulated-annealing-based constrained alloy design into a single closed-loop workflow. A conversational layer exposes the same prediction and design engines through natural-language commands, and a document-ingestion pipeline converts unstructured literature into structured composition–property records. Every prediction is paired with dual-provenance neighbour views and screened by an inline LLM feasibility gate that checks classical metallurgical heuristics before inference. By coupling domain-aware visualisation with interactive inference and design, MATAI lowers the engineering burden of applying AI to materials science and provides a reusable blueprint for closed-loop AI-for-Science systems.