Machine Learning‐Driven Nanoscale Synthesis for Electrocatalytic Performance: From Data‐Driven Methodologies to Closed‐Loop Optimization

The rational design of functional nanomaterials is fundamentally challenged by complex synthesis-structure-performance relationships and vast design spaces that defy conventional trial-and-error methods. In particular, nanomaterials have become integral to electrocatalysis, where their tunable surface structures and quantum-scale effects govern catalytic activity and selectivity. Nevertheless, translating their intrinsic physicochemical advantages into catalytic performance remains difficult, as it requires precise control over synthetic parameters to access desired surface structures and active sites. Machine learning (ML) has emerged as a transformative framework, integrating predictive modeling, data-driven synthesis optimization, and autonomous experimentation to accelerate the discovery of high-performance nanocatalysts. This review outlines how ML provides a unified foundation for nanomaterials research by integrating data curation, algorithmic development, and application-specific modeling. It also enables controllable synthesis through reaction condition optimization, multimodal descriptor learning, and autonomous experimentation, while linking structural complexity to catalytic function via interpretable learning frameworks. Building on these capabilities, ML is redefining materials innovation through physics-informed generative models, autonomous platforms, and multiscale interpretability. These advances collectively support closed-loop, end-to-end strategies for nanocatalyst design by integrating precision synthesis, model-guided optimization, and multimodal characterization. Together, they lay the foundation for a new paradigm in the discovery of intelligent nanomaterials.