Lecture
This lecture discusses the prediction of reaction yields using deep learning models, focusing on the challenges of identifying reactants and reagents in chemical reactions. The instructor explains how machine learning representations, such as graph-based structures, are used to analyze and predict reaction outcomes. The lecture also covers the importance of high-quality data sets in training models, the impact of additives on reaction yields, and the use of natural language processing for text-based representations in chemistry. The presentation highlights the transferability of trained models across different reaction classes and the significance of data quality in yield prediction tasks.