用可执行合成路径代替原子结构，推动材料发现新范式
Beyond Structure: Revolutionising Materials Discovery via AI-Driven Synthesis Protocol-Property Relationships
- 将合成方案作为核心设计变量，构建机器可读的反应流程
- 提出生成与逆向设计模型，自动生成可行动的合成路径
- 适合材料研发、自动化实验平台及可持续材料设计者
当前人工智能驱动材料发现以结构为中心的范式，尽管已产生数千种候选结构，却在关键瓶颈——可合成性差距处停滞。我们主张转向以合成优先的新范式，将可执行的合成协议而非仅原子构型作为首要设计变量。该路线图基于三大支柱：(i) 将合成过程表示为机器可读协议；(ii) 部署生成与逆向设计模型，提出可操作的反应路径与配方；(iii) 集成闭环优化，使协议在实验现实与可持续性约束下持续改进。以协议P→结构X→性质y的因果框架为基础，本文提出方法论基础、标准需求及自驱动实验室（SDL）集成策略，推动可复现、数据驱动的材料发现加速。
原文摘要 · Abstract (English)
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.
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