In this comprehensive review, we delve into the principles, revolutionary methods, and techniques employed in the structural design and determination of COFs, to decipher the underlying logic governing their captivating properties.

With the development of Industry 4.0, artificial intelligence (AI) is gaining increasing attention for its performance in solving particularly complex problems in industrial chemistry and chemical engineering. Therefore, this review provides an overview of the application of AI techniques, in particular machine learning, in chemical design, synthesis, and process optimization over the past years. In this review, the focus is on the application of AI for structure-function relationship analysis, synthetic route planning, and automated synthesis. Finally, we discuss the challenges and future of AI in making chemical products.

An effective and rapid deep learning method to predict chemical reactions contributes to the research and development of organic chemistry and drug discovery.

We propose the SolvBERT model, an attention-based NLP model that predicts multiple solvation properties.