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From: Interpretable physics-informed machine learning approaches to accelerate electrocatalyst development
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.
