Machine learning (ML) algorithms are increasingly being used in catalysis to predict reaction outcomes and design new catalysts. ML models can be trained on experimental and simulation data to predict the properties and performance of catalysts. This approach accelerates the discovery process by reducing the need for extensive experimental trials. Techniques such as neural networks and support vector machines are particularly useful in this context.
