Keywords
health
Abstract
This review examines domain-grounded formula recommendation for catalyst composition design through a mechanism-to-decision framework. It asks which retrieval and graph signals should govern composition suggestions. Evidence is organized around representation, validation, uncertainty, and operational control, with no invented experiments or unreported quantitative results. Particular attention is given to domain adaptation reproducing formula frequency instead of mechanism. The synthesis shows that credible translation depends on explicit system boundaries, source-level traceability, failure-aware evaluation, and revalidation triggers. These principles provide a disciplined basis for composition and formulation support while keeping component promise distinct from system readiness.
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