Machine learning algorithms can identify patterns within large datasets that would be virtually impossible for humans to discern. In catalyst design, these algorithms can be used to predict the activity, selectivity, and stability of different catalyst compositions. By training models on experimental data, machine learning models can suggest new catalyst formulations that are likely to exhibit desirable properties, thereby reducing the time and cost associated with experimental trial-and-error.
