Unsupervised learning is advantageous when dealing with complex, high-dimensional data typically found in catalysis research. This includes datasets generated from high-throughput screening experiments, spectroscopy, and computational simulations. By applying unsupervised learning algorithms, researchers can uncover underlying patterns, group similar data points, and even suggest new hypotheses without the need for human intervention.
