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opportunity to reshape the landscape of materials research. Rather than merely automating tasks, looking
ahead, future AI-Agents may evolve into collaborative research partners, capable of proposing hypotheses,
reasoning across multi-scale datasets, and autonomously navigating vast chemical and structural design
spaces. Achieving this vision will depend on sustained progress in foundational AI models, standardized
simulation interfaces, and curated domain knowledge bases encompassing both experimental and theoretical
insights. With the advancement and maturation of these technologies, we anticipate that adaptive,
self-improving agents will play an increasingly pivotal role in accelerating materials discovery, enhancing
reproducibility, and enabling more intelligent, goal-driven materials design workflows.

DECLARATIONS
Authors’ contributions
Made substantial contributions to the conception and design of the study and performed data analysis and
interpretation: Yang, M.; Zeng, Q.; Wang, X.
Performed data acquisition and provided administrative, technical, and material support: Xu, D. H.; Zhang,
L.; Jiang, G.

Availability of data and materials
The data that support the findings of this study are available from the corresponding author upon reasonable
request. Code for this demo is available at h​t​t​p​s​:​/​/​g​i​t​h​u​b​.​c​o​m​/​Y​a​n​g​-​c​o​l​-​l​a​b​/​R​e​q​u​e​s​t​-​d​r​i​v​e​n​-​w​o​r​k​f​l​o​w​-​a​u​t​o​m
a​t​i​o​n​-​d​e​m​o​.

Financial support and sponsorship
Yang, M. acknowledges the National Natural Science Foundation of China (Grant No. 22173064). Wang, X.
acknowledges the Advanced Materials National Science and Technology Major Project (Grant No.
2025ZD0618403).

Conflicts of interest
All authors declared that there are no conflicts of interest.

Ethical approval and consent to participate
Not applicable.

Consent for publication
Not applicable.

Copyright
© The Author(s) 2026.

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