Keywords
materials
Abstract
This review examines personalized graph recommendation for electrocatalyst synthesis planning through a mechanism-to-decision framework. It asks how prior laboratory context should inform candidate ranking without amplifying historical popularity. Evidence is organized around representation, validation, uncertainty, and operational control, with no invented experiments or unreported quantitative results. Particular attention is given to recommendation feedback loops narrowing chemical exploration. 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 data-guided electrocatalyst synthesis while keeping component promise distinct from system readiness.
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