How Does Machine Learning Improve Catalyst Design?
Machine learning improves catalyst design by using algorithms to analyze existing data on catalyst performance and properties. This can involve supervised learning, where models are trained on labeled datasets, or unsupervised learning, which identifies patterns in unlabeled data. By leveraging these techniques, researchers can predict the performance of new catalysts and optimize existing ones, reducing the need for extensive experimental trials.
