Designing effective catalysts involves several challenges. One significant challenge is achieving a balance between activity, selectivity, and stability. High activity ensures that the catalyst speeds up the reaction efficiently, while high selectivity ensures that the desired product is formed preferentially. Stability is important for the catalyst to maintain its performance over time. Researchers use a combination of experimental and computational approaches to overcome these challenges. For example, high-throughput screening methods allow for the rapid evaluation of large numbers of catalyst candidates, while machine learning algorithms can predict catalyst performance based on existing data.
