Dynamic Machine Learning‐Driven Optimization of Microwave‐Synthesized Photocatalysts for Enhanced Hydrogen Peroxide Production

Abstract Photocatalysis offers an energy‐efficient and sustainable solution to environmental pollution and energy shortages. The core of this process lies in photocatalysis. However, establishing a clear relationship between their structure and performance through traditional experimental methods is often time‐intensive and labor‐intensive. Machine learning (ML) has recently gained traction in guiding photocatalyst synthesis, though it is often challenged by limited data availability. This study introduces a dynamic ML‐guided approach that iteratively optimizes experimental parameters through successive cycles of ML analysis and experimentation, effectively circumventing the need for large datasets. Applied to the synthesis of photocatalysts via microwave heating of quercetin, this method yielded optimal performance after three iterations, achieving a high hydrogen peroxide production rate. This ML approach demonstrates an effective few‐shot ML optimization strategy for catalyst synthesis.