The integration of artificial intelligence (AI) with high-throughput experimentation (HTE) techniques is revolutionizing catalyst design

addressing challenges in efficiency

cost

and scalability. This review explores the synergistic application of AI and HTE

highlighting their role in accelerating catalyst discovery

optimizing reaction parameters

and understanding structure-performance relationships. HTE facilitates the rapid preparation

characterization

and evaluation of diverse catalyst formulations

generating large datasets essential for AI model training. Machine learning algorithms

including regression models

neural networks

and active learning frameworks

analyze these datasets to uncover the underlying relationships between the data

predict performance

and optimize experimental workflows in real-time. Case studies across heterogeneous

homogeneous

and electrocatalysis demonstrate significant advancements

including improved reaction selectivity

enhanced material stability

and shorten discovery cycles. The integration of AI with HTE has significantly accelerated discovery cycles

enabling the optimization of catalyst formulations and reaction conditions. Despite these achievements

challenges remain

including reliance on researcher expertise

real-time adaptability

and the complexity of large-scale data analysis. Addressing these limitations through refined experimental protocols

standardized datasets

and interpretable AI models will unlock the full potential of AI-HTE integration.