# Beyond structure: revolutionising materials discovery via AI-driven synthesis protocol-property relationships

https://mdr.nims.go.jp/datasets/db14c5de-27d9-4758-8395-d61a6204fe42

## Files

- [Lambard_2026_J._Phys._Mater._9_021003.pdf](https://mdr.nims.go.jp/filesets/2d567b0f-7136-4cc6-864c-f32aab1444b0/download) ([Detail](https://mdr.nims.go.jp/filesets/2d567b0f-7136-4cc6-864c-f32aab1444b0.md))

## Id

db14c5de-27d9-4758-8395-d61a6204fe42

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-06-17T01:51:48.605397Z

## Updated at

2026-06-17T02:21:44.229996Z

## Published at

2026-06-17T03:40:04.242137Z

## Doi



## First published url

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

## Date published

2026-06-01

## Recorded date published

2026-6-1

## Resource type

journal_article

## Manuscript type

accepted_manuscript

## Collection



## Title

- title: 'Beyond structure: revolutionising materials discovery via AI-driven synthesis
    protocol-property relationships'
  title_type: original
  lang: en

## Description

- description: '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.'
  description_type: abstract
  lang: und

## Creator

- name: Guillaume Lambard
  role: author
  orcid: https://orcid.org/0000-0003-0275-4079

## Contact agent



## Publisher

organization: National Institute for Materials Science
ror: https://ror.org/

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## Keyword

- subject: materials discovery
  schema: not_defined
- subject: beyond structure
  schema: not_defined
- subject: AI-driven
  schema: not_defined
- subject: AI
  schema: not_defined
- subject: synthesis protocol
  schema: not_defined
- subject: synthesis protocol-property relationships
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: 'Journal of Physics: Materials'
  issn: '25157639'
  volume: '9'
  issue: '2'
  article_number: '021003'

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## Instrument



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## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



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## Fileset

- id: 2d567b0f-7136-4cc6-864c-f32aab1444b0
  filename: Lambard_2026_J._Phys._Mater._9_021003.pdf
  content_type: application/pdf
  size: 802324
  md5: a096084325fba6f3355faa463bc29a1e

## Thumbnail

fileset_id: 2d567b0f-7136-4cc6-864c-f32aab1444b0
filename: Lambard_2026_J._Phys._Mater._9_021003.pdf