Physical and Theoretical Chemistry

Artificial Intelligence and Data-Driven Chemistry

This track explores the use of machine learning, artificial intelligence, automation, and chemical databases in modern research. Presentations may cover property prediction, reaction forecasting, molecular generation, active learning, and laboratory robotics. Researchers will discuss data quality, model interpretation, uncertainty estimation, and reproducibility. The combination of AI predictions with quantum calculations and experimental testing will be emphasized. Applications may include catalyst discovery, materials development, drug design, and process optimization. The track will examine how digital tools can accelerate chemical research responsibly.