🤖 AI Summary
This work addresses the "synthesis gap" in AI-driven materials discovery—the disconnect arising from neglecting synthetic feasibility—by introducing a "synthesis-first" paradigm. It uniquely treats machine-readable synthesis protocols as primary design variables, establishing a causal framework that maps synthesis protocols (P) → structure (X) → performance (y). By integrating generative and inverse design models, closed-loop optimization algorithms, and self-driving laboratory technologies, the approach enables concurrent optimization of synthesis pathways and material properties. This strategy not only bridges the longstanding divide between computational design and experimental realization but also provides a systematic, data-driven methodology for reproducible and sustainable materials discovery.
📝 Abstract
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.