Language models for accelerated chemical discovery and synthesis
Philippe Schwaller a
a epfl
Materials for Sustainable Development Conference (MATSUS)
Proceedings of MATSUS Fall 2024 Conference (MATSUSFall24)
#SOLTEC - Solar Technologies for Renewable Fuels and Chemicals: On the Way to Industrial Implementation
Lausanne, Switzerland, 2024 November 12th - 15th
Organizers: Víctor A. de la Peña O'Shea and Miguel García-Tecedor
Invited Speaker, Philippe Schwaller, presentation 408
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.

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