Machine learning (ML) has emerged as a powerful tool to enhance the accuracy of catalytic models. By leveraging large datasets, ML algorithms can identify patterns and correlations that are not easily discernible through traditional methods. Techniques such as neural networks and regression analysis can be applied to predict catalytic behavior with higher accuracy and lower computational cost.
