Deep learning for complex chemical systems.

This article discusses the use of deep learning (DL) in complex chemical systems. DL models, such as convolutional neural networks and recurrent neural networks, are increasingly popular in the chemistry community because they automatically learn underlying representations. These models utilize nonlinear activation functions to capture the relationship between input descriptors and outputs. DL is used to predict properties, model reactions, and optimize synthetic processes. It can also be combined with simulations and machine learning to create intelligent laboratories for rapid discovery of reactions and functional materials. The article emphasizes the need for a user-friendly platform and the sharing of code and datasets to further advance DL in chemistry. [Extracted from the article]