Division/Committee: [CINF: Division of Chemical Information] [CINF: Division of Chemical Information] [COMP: Division of Computers in Chemistry]

Machine learning (ML) has emerged as a powerful tool in the field of chemistry, offering innovative approaches to address complex challenges related to chemical mechanisms, prediction, and discovery. The proposed symposium will explore the cutting-edge integration of ML in various domains of chemistry, including the development of Quantitative Structure-Property Relationships (QSPR) for accurate property prediction, the creation of ML-based interatomic potentials for enhanced molecular simulations, and the acceleration of chemical discovery through novel deep learning approaches. Furthermore, cutting-edge topics related to the applications of large language models (LLMs) in chemistry, promising a new paradigm in understanding and generating molecular structures, will also be included. Lastly, the event will highlight the significance of integrated computational-experiment platforms that synergize predictive modeling, high-throughput experiments, and real-time data analysis to revolutionize the development, optimization, and understanding of new chemicals. This symposium aims to bring together researchers applying ML in various areas and discuss the transformative impact of ML on chemistry, fostering collaboration and inspiring future innovations in these vital fields.

Dr Rachelle Bienstock

Senior Discovery Services Scientist, RJB Computational Modeling LLC

Appearances