Machine learning can play a critical role in catalyst design by analyzing previous experimental data and predicting the properties of new catalyst materials. Techniques such as neural networks and support vector machines can be used to model the relationship between catalyst structure and its performance. This can significantly reduce the time and cost associated with experimental catalyst development.
