Data-driven catalysis involves the use of machine learning, artificial intelligence, and other computational techniques to analyze experimental and simulation data. These methods identify trends and correlations that might not be apparent through traditional experimental approaches. The ultimate goal is to create predictive models that can guide the design and optimization of catalysts.
