Experimental data can drive the rational design and optimization of catalysts. By correlating structural features with catalytic performance, researchers can identify key factors that influence activity and selectivity. This information can be used to modify existing catalysts or design new materials with improved properties. Additionally, data-driven approaches such as machine learning can accelerate the discovery of novel catalysts.
