Beyond Structure: Revolutionising Materials Discovery via AI-Driven Synthesis Protocol-Property Relationships
This perspective addresses the bottleneck of synthesizability in materials discovery for the materials science community, but remains a conceptual roadmap without empirical validation.
The paper argues that AI-driven materials discovery is hindered by a 'synthesizability gap' and proposes a shift to a synthesis-first paradigm where executable synthesis protocols are primary design variables. It outlines a roadmap with three pillars: machine-readable protocol representation, generative models for reaction pathways, and closed-loop optimization.
The current structure-centric paradigm in artificial intelligence (AI)-driven materials discovery, despite delivering thousands of candidate structures, is stalling at a critical barrier: the synthesizability 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 (SDL) integration strategies to accelerate reproducible, data-first materials discovery.