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説明:

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.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: materials discovery, beyond structure, AI-driven, AI, synthesis protocol, synthesis protocol-property relationships

刊行年月日: 2026-06-01

出版者: National Institute for Materials Science

掲載誌:

• Journal of Physics: Materials (ISSN: 25157639) vol. 9 issue. 2 021003

研究助成金:

原稿種別: 著者最終稿 (Accepted manuscript)

MDR DOI:

公開URL: https://doi.org/10.1088/2515-7639/ae6e72

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更新時刻: 2026-06-17 11:21:44 +0900

MDRでの公開時刻: 2026-06-17 12:40:04 +0900