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Wu et al. J. Mater. Inf. 2025, 5, 15 https://dx.doi.org/10.20517/jmi.2024.67 Page 11 of 15

Figure 4. Schematic diagram of PIML applications in an A-lab environment. The process begins with researchers posing their
requirements through LLMs, which generate physical parameters for PIML-based experimental predictions. These predictions guide
robotic synthesis while PIML generative models simultaneously predict candidate structures. The resulting materials and structural data
are validated and screened into the database, enabling active learning to continuously improve the PIML model. PIML: Physics-informed
machine learning; A-lab: automated laboratory; LLMs: large langue models.

DECLARATIONS
Authors’ contributions
Original draft: Wu, H.
Visualization, writing editing: Wu, H.
Review and editing: Chen, M., Cheng, H., Yang, T., Zeng, M., Yang, M.
Funding acquisition: Yang, M.

Availability of data and materials
All detailed materials that support the findings are available from the corresponding author upon
reasonable request.

Financial support and sponsorship
This work was supported by the Hong Kong Polytechnic University (project numbers: P0042711, P0042711
and P0048122) and Guangdong Natural Science Foundation (project number: 2024A1515010031).

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) 2025.