Beyond structure: revolutionising materials discovery via AI-driven synthesis protocol-property relationships

Abstract The current structure-centric paradigm in artificial intelligence-driven materials discovery, despite delivering thousands of candidate structures, is stalling at a critical barrier: the synthesisability gap. We argue that closing this gap demands a pivot to a synthesis-first paradigm in which executable synthesis protocols, not just atomic configurations, are treated as primary design variables. We outline a roadmap built on three pillars: (i) representing synthesis procedures as machine-readable protocols, (ii) deploying generative and inverse-design models to propose actionable reaction pathways and recipes, and (iii) integrating closed-loop optimisation to refine protocols against experimental realities and sustainability constraints. Framed in terms of the causal backbone P → X → y from protocol P to structure X and properties y , this perspective sets out methodological building blocks, standards needs and self-driving laboratory integration strategies to accelerate reproducible, data-first materials discovery.