Molecular Geometry

Molecular geometry research focuses on accurately representing and predicting the three-dimensional structures of molecules, crucial for understanding their properties and behavior. Current efforts concentrate on developing advanced machine learning models, including graph neural networks, diffusion models, and Bayesian flow networks, to efficiently generate and analyze molecular geometries from various data sources, often incorporating information about interatomic forces and symmetries. These advancements are significantly impacting drug discovery and materials science by enabling faster and more accurate prediction of molecular properties and facilitating the design of novel molecules with desired characteristics. The development of robust and efficient methods for handling large molecular systems and diverse chemical spaces remains a key challenge.