Supervised learning techniques can significantly accelerate the research and development process in catalysis. By using historical data, these models can predict reaction rates, selectivity, and yield with high accuracy. This reduces the need for extensive experimental trials, saving both time and resources. Additionally, machine learning can identify patterns and correlations that might be overlooked by human researchers.
