Proceedings of MATSUS Fall 2024 Conference (MATSUSFall24)
DOI: https://doi.org/10.29363/nanoge.matsusfall.2024.408
Publication date: 28th August 2024
AI-accelerated synthesis is an emerging field that uses machine learning algorithms to improve the efficiency and productivity of chemical and materials synthesis. Modern machine learning models, such as (large) language models, can capture the knowledge hidden in large chemical databases to rapidly design and discover new compounds, predict the outcome of reactions, and help optimize chemical reactions. One of the key advantages of AI-accelerated synthesis is its ability to make vast chemical data accessible and predict promising candidate synthesis paths, potentially leading to breakthrough discoveries. Overall, AI is poised to revolutionise the field of organic synthesis, enabling faster and more efficient drug development, catalysis, and other applications.