Traditional catalyst design often involves trial-and-error methods, which can be time-consuming and costly. ML and AI can significantly accelerate this process by predicting the performance of potential catalysts before they are synthesized. Algorithms can analyze vast amounts of data from previous experiments to identify promising candidates, optimizing factors such as stability, activity, and selectivity. This approach not only reduces the time and cost associated with catalyst development but also opens up new avenues for discovering novel catalytic materials.
