Machine learning (ML) is increasingly being integrated into catalysis research to analyze large datasets and predict catalytic performance. By training algorithms on experimental data, researchers can develop models that identify patterns and make predictions about new catalysts. ML techniques such as neural networks and regression analysis are used to optimize reaction conditions and design more efficient catalysts. This approach enhances the ability to explore vast chemical spaces and accelerates catalyst development.
