3D Atomic System

3D atomic systems are being modeled using machine learning to accelerate materials discovery and design, focusing on predicting properties and generating novel structures. Current research emphasizes the development and refinement of generative models, such as diffusion models and graph neural networks (GNNs), particularly geometric GNNs which incorporate spatial information and symmetries, along with Bayesian optimization techniques to efficiently explore complex energy landscapes. These advancements are improving the accuracy and efficiency of molecular simulations and enabling the prediction of material properties and the design of new molecules and materials with desired characteristics, impacting fields like drug discovery and materials science.