Beyond Structure: Revolutionising Materials Discovery via AI-Driven Synthesis Protocol-Property Relationships

TL;DR

This paper advocates shifting from a structure-centric approach to a synthesis-first paradigm in AI-driven materials discovery, emphasizing executable protocols and closed-loop optimization to bridge the synthesizability gap.

Contribution

It proposes a new framework integrating machine-readable synthesis protocols, generative models, and closed-loop optimization to enhance reproducibility and real-world applicability in materials discovery.

Findings

Framework for representing synthesis as machine-readable protocols

Use of generative models to propose reaction pathways

Integration of closed-loop optimization for protocol refinement

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.